Posts Tagged ‘MRI’

Imaging-guided cancer treatment

Imaging-guided cancer treatment

Writer & reporter: Dror Nir, PhD

It is estimated that the medical imaging market will exceed $30 billion in 2014 (FierceMedicalImaging). To put this amount in perspective; the global pharmaceutical market size for the same year is expected to be ~$1 trillion (IMS) while the global health care spending as a percentage of Gross Domestic Product (GDP) will average 10.5% globally in 2014 (Deloitte); it will reach ~$3 trillion in the USA.

Recent technology-advances, mainly miniaturization and improvement in electronic-processing components is driving increased introduction of innovative medical-imaging devices into critical nodes of major-diseases’ management pathways. Consequently, in contrast to it’s very small contribution to global health costs, medical imaging bears outstanding potential to reduce the future growth in spending on major segments in this market mainly: Drugs development and regulation (e.g. companion diagnostics and imaging surrogate markers); Disease management (e.g. non-invasive diagnosis, guided treatment and non-invasive follow-ups); and Monitoring aging-population (e.g. Imaging-based domestic sensors).

In; The Role of Medical Imaging in Personalized Medicine I discussed in length the role medical imaging assumes in drugs development.  Integrating imaging into drug development processes, specifically at the early stages of drug discovery, as well as for monitoring drug delivery and the response of targeted processes to the therapy is a growing trend. A nice (and short) review highlighting the processes, opportunities, and challenges of medical imaging in new drug development is: Medical imaging in new drug clinical development.

The following is dedicated to the role of imaging in guiding treatment.

Precise treatment is a major pillar of modern medicine. An important aspect to enable accurate administration of treatment is complementing the accurate identification of the organ location that needs to be treated with a system and methods that ensure application of treatment only, or mainly to, that location. Imaging is off-course, a major component in such composite systems. Amongst the available solution, functional-imaging modalities are gaining traction. Specifically, molecular imaging (e.g. PET, MRS) allows the visual representation, characterization, and quantification of biological processes at the cellular and subcellular levels within intact living organisms. In oncology, it can be used to depict the abnormal molecules as well as the aberrant interactions of altered molecules on which cancers depend. Being able to detect such fundamental finger-prints of cancer is key to improved matching between drugs-based treatment and disease. Moreover, imaging-based quantified monitoring of changes in tumor metabolism and its microenvironment could provide real-time non-invasive tool to predict the evolution and progression of primary tumors, as well as the development of tumor metastases.

A recent review-paper: Image-guided interventional therapy for cancer with radiotherapeutic nanoparticles nicely illustrates the role of imaging in treatment guidance through a comprehensive discussion of; Image-guided radiotherapeutic using intravenous nanoparticles for the delivery of localized radiation to solid cancer tumors.

 Graphical abstract


One of the major limitations of current cancer therapy is the inability to deliver tumoricidal agents throughout the entire tumor mass using traditional intravenous administration. Nanoparticles carrying beta-emitting therapeutic radionuclides [DN: radioactive isotops that emits electrons as part of the decay process a list of β-emitting radionuclides used in radiotherapeutic nanoparticle preparation is given in table1 of this paper.) that are delivered using advanced image-guidance have significant potential to improve solid tumor therapy. The use of image-guidance in combination with nanoparticle carriers can improve the delivery of localized radiation to tumors. Nanoparticles labeled with certain beta-emitting radionuclides are intrinsically theranostic agents that can provide information regarding distribution and regional dosimetry within the tumor and the body. Image-guided thermal therapy results in increased uptake of intravenous nanoparticles within tumors, improving therapy. In addition, nanoparticles are ideal carriers for direct intratumoral infusion of beta-emitting radionuclides by convection enhanced delivery, permitting the delivery of localized therapeutic radiation without the requirement of the radionuclide exiting from the nanoparticle. With this approach, very high doses of radiation can be delivered to solid tumors while sparing normal organs. Recent technological developments in image-guidance, convection enhanced delivery and newly developed nanoparticles carrying beta-emitting radionuclides will be reviewed. Examples will be shown describing how this new approach has promise for the treatment of brain, head and neck, and other types of solid tumors.

The challenges this review discusses

  • intravenously administered drugs are inhibited in their intratumoral penetration by high interstitial pressures which prevent diffusion of drugs from the blood circulation into the tumor tissue [1–5].
  • relatively rapid clearance of intravenously administered drugs from the blood circulation by kidneys and liver.
  • drugs that do reach the solid tumor by diffusion are inhomogeneously distributed at the micro-scale – This cannot be overcome by simply administering larger systemic doses as toxicity to normal organs is generally the dose limiting factor.
  • even nanoparticulate drugs have poor penetration from the vascular compartment into the tumor and the nanoparticles that do penetrate are most often heterogeneously distributed

How imaging could mitigate the above mentioned challenges

  • The inclusion of an imaging probe during drug development can aid in determining the clearance kinetics and tissue distribution of the drug non-invasively. Such probe can also be used to determine the likelihood of the drug reaching the tumor and to what extent.

Note: Drugs that have increased accumulation within the targeted site are likely to be more effective as compared with others. In that respect, Nanoparticle-based drugs have an additional advantage over free drugs with their potential to be multifunctional carriers capable of carrying both therapeutic and diagnostic imaging probes (theranostic) in the same nanocarrier. These multifunctional nanoparticles can serve as theranostic agents and facilitate personalized treatment planning.

  • Imaging can also be used for localization of the tumor to improve the placement of a catheter or external device within tumors to cause cell death through thermal ablation or oxidative stress secondary to reactive oxygen species.

See the example of Vintfolide in The Role of Medical Imaging in Personalized Medicine


Note: Image guided thermal ablation methods include radiofrequency (RF) ablation, microwave ablation or high intensity focused ultrasound (HIFU). Photodynamic therapy methods using external light devices to activate photosensitizing agents can also be used to treat superficial tumors or deeper tumors when used with endoscopic catheters.

  • Quality control during and post treatment

For example: The use of high intensity focused ultrasound (HIFU) combined with nanoparticle therapeutics: HIFU is applied to improve drug delivery and to trigger drug release from nanoparticles. Gas-bubbles are playing the role of the drug’s nano-carrier. These are used both to increase the drug transport into the cell and as ultrasound-imaging contrast material. The ultrasound is also used for processes of drug-release and ablation.


Additional example; Multifunctional nanoparticles for tracking CED (convection enhanced delivery)  distribution within tumors: Nanoparticle that could serve as a carrier not only for the therapeutic radionuclides but simultaneously also for a therapeutic drug and 4 different types of imaging contrast agents including an MRI contrast agent, PET and SPECT nuclear diagnostic imaging agents and optical contrast agents as shown below. The ability to perform multiple types of imaging on the same nanoparticles will allow studies investigating the distribution and retention of nanoparticles initially in vivo using non-invasive imaging and later at the histological level using optical imaging.



Image-guided radiotherapeutic nanoparticles have significant potential for solid tumor cancer therapy. The current success of this therapy in animals is most likely due to the improved accumulation, retention and dispersion of nanoparticles within solid tumor following image-guided therapies as well as the micro-field of the β-particle which reduces the requirement of perfectly homogeneous tumor coverage. It is also possible that the intratumoral distribution of nanoparticles may benefit from their uptake by intratumoral macrophages although more research is required to determine the importance of this aspect of intratumoral radionuclide nanoparticle therapy. This new approach to cancer therapy is a fertile ground for many new technological developments as well as for new understandings in the basic biology of cancer therapy. The clinical success of this approach will depend on progress in many areas of interdisciplinary research including imaging technology, nanoparticle technology, computer and robot assisted image-guided application of therapies, radiation physics and oncology. Close collaboration of a wide variety of scientists and physicians including chemists, nanotechnologists, drug delivery experts, radiation physicists, robotics and software experts, toxicologists, surgeons, imaging physicians, and oncologists will best facilitate the implementation of this novel approach to the treatment of cancer in the clinical environment. Image-guided nanoparticle therapies including those with β-emission radionuclide nanoparticles have excellent promise to significantly impact clinical cancer therapy and advance the field of drug delivery.


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Current Advanced Research Topics in MRI-based Management of Cancer Patients

 Author: Dror Nir, PhD

Step forward towards quantitative and reproducible MRI of cancer patients is the combination of structure and morphology based imaging with expressions of typical bio-chemical processes using imaging contrast materials. The following list brings the latest publications on this subject in Radiology magazine.

 The Effects of Applying Breast Compression in Dynamic Contrast Material–enhanced MR Imaging


 Purpose: To evaluate the effects of breast compression on breast cancer masses, contrast material enhancement of glandular tissue, and quality of magnetic resonance (MR) images in the identification and characterization of breast lesions.

Materials and Methods: This was a HIPAA-compliant, institutional review board–approved retrospective study, with waiver of informed consent. Images from 300 MR imaging examinations in 149 women (mean age ± standard deviation, 51.5 years ± 10.9; age range, 22–76 years) were evaluated. The women underwent diagnostic MR imaging (no compression) and MR-guided biopsy (with compression) between June 2008 and February 2013. Breast compression was expressed as a percentage relative to the noncompressed breast. Percentage enhancement difference was calculated between noncompressed- and compressed-breast images obtained in early and delayed contrast-enhanced phases. Breast density, lesion type (mass vs non-masslike enhancement [NMLE]), lesion size, percentage compression, and kinetic curve type were evaluated. Linear regression, receiver operating characteristic (ROC) curve analysis, and κ test were performed.

Conclusion: Breast compression during biopsy affected breast lesion detection, lesion size, and dynamic contrast-enhanced MR imaging interpretation and performance. Limiting the application of breast compression is recommended, except when clinically necessary.

 Localized Prostate Cancer Detection with 18F FACBC PET/CT: Comparison with MR Imaging and Histopathologic Analysis


 Purpose: To characterize uptake of 1-amino-3-fluorine 18-fluorocyclobutane-1-carboxylic acid (18F FACBC) in patients with localized prostate cancer, benign prostatic hyperplasia (BPH), and normal prostate tissue and to evaluate its potential utility in delineation of intraprostatic cancers in histopathologically confirmed localized prostate cancer in comparison with magnetic resonance (MR) imaging.

Materials and Methods: Institutional review board approval and written informed consent were obtained for this HIPAA-compliant prospective study. Twenty-one men underwent dynamic and static abdominopelvic 18F FACBC combined positron emission tomography (PET) and computed tomography (CT) and multiparametric (MP) 3-T endorectal MR imaging before robotic-assisted prostatectomy. PET/CT and MR images were coregistered by using pelvic bones as fiducial markers; this was followed by manual adjustments. Whole-mount histopathologic specimens were sliced with an MR-based patient-specific mold. 18F FACBC PET standardized uptake values (SUVs) were compared with those at MR imaging and histopathologic analysis for lesion- and sector-based (20 sectors per patient) analysis. Positive and negative predictive values for each modality were estimated by using generalized estimating equations with logit link function and working independence correlation structure.

Conclusion: 18F FACBC PET/CT shows higher uptake in intraprostatic tumor foci than in normal prostate tissue; however, 18F FACBC uptake in tumors is similar to that in BPH nodules. Thus, it is not specific for prostate cancer. Nevertheless, combined 18F FACBC PET/CT and T2-weighted MR imaging enable more accurate localization of prostate cancer lesions than either modality alone.

Illuminating Radiogenomic Characteristics of Glioblastoma Multiforme through Integration of MR Imaging, Messenger RNA Expression, and DNA Copy Number Variation


Purpose: To perform a multilevel radiogenomics study to elucidate the glioblastoma multiforme (GBM) magnetic resonance (MR) imaging radiogenomic signatures resulting from changes in messenger RNA (mRNA) expression and DNA copy number variation (CNV).

Materials and Methods: Radiogenomic analysis was performed at MR imaging in 23 patients with GBM in this retrospective institutional review board–approved HIPAA-compliant study. Six MR imaging features—contrast enhancement, necrosis, contrast-to-necrosis ratio, infiltrative versus edematous T2 abnormality, mass effect, and subventricular zone (SVZ) involvement—were independently evaluated and correlated with matched genomic profiles (global mRNA expression and DNA copy number profiles) in a significant manner that also accounted for multiple hypothesis testing by using gene set enrichment analysis (GSEA), resampling statistics, and analysis of variance to gain further insight into the radiogenomic signatures in patients with GBM

Conclusion: Construction of an MR imaging, mRNA, and CNV radiogenomic association map has led to identification of MR traits that are associated with some known high-grade glioma biomarkers and association with genomic biomarkers that have been identified for other malignancies but not GBM. Thus, the traits and genes identified on this map highlight new candidate radiogenomic biomarkers for further evaluation in future studies.

PET/MR Imaging: Technical Aspects and Potential Clinical Applications


Instruments that combine positron emission tomography (PET) and magnetic resonance (MR) imaging have recently been assembled for use in humans, and may have diagnostic performance superior to that of PET/computed tomography (CT) for particular clinical and research applications. MR imaging has major strengths compared with CT, including superior soft-tissue contrast resolution, multiplanar image acquisition, and functional imaging capability through specialized techniques such as diffusion-tensor imaging, diffusion-weighted (DW) imaging, functional MR imaging, MR elastography, MR spectroscopy, perfusion-weighted imaging, MR imaging with very short echo times, and the availability of some targeted MR imaging contrast agents. Furthermore, the lack of ionizing radiation from MR imaging is highly appealing, particularly when pediatric, young adult, or pregnant patients are to be imaged, and the safety profile of MR imaging contrast agents compares very favorably with iodinated CT contrast agents. MR imaging also can be used to guide PET image reconstruction, partial volume correction, and motion compensation for more accurate disease quantification and can improve anatomic localization of sites of radiotracer uptake, improve diagnostic performance, and provide for comprehensive regional and global structural, functional, and molecular assessment of various clinical disorders. In this review, we discuss the historical development, software-based registration, instrumentation and design, quantification issues, potential clinical applications, potential clinical roles of image segmentation and global disease assessment, and challenges related to PET/MR imaging.

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The importance of spatially-localized and quantified image interpretation in cancer management

Writer & reporter: Dror Nir, PhD

I became involved in the development of quantified imaging-based tissue characterization more than a decade ago. From the start, it was clear to me that what clinicians needs will not be answered by just identifying whether a certain organ harbors cancer. If imaging devices are to play a significant role in future medicine, as a complementary source of information to bio-markers and gene sequencing the minimum value expected of them is accurate directing of biopsy needles and treatment tools to the malignant locations in the organ.  Therefore, the design goal of the first Prostate-HistoScanning (“PHS”) version I went into the trouble of characterizing localized volume of tissue at the level of approximately 0.1cc (1x1x1 mm). Thanks to that, the imaging-interpretation overlay of PHS localizes the suspicious lesions with accuracy of 5mm within the prostate gland; Detection, localisation and characterisation of prostate cancer by prostate HistoScanning(™).

I then started a more ambitious research aiming to explore the feasibility of identifying sub-structures within the cancer lesion itself. The preliminary results of this exploration were so promising that it surprised not only the clinicians I was working with but also myself. It seems, that using quality ultrasound, one can find Imaging-Biomarkers that allows differentiation of inside structures of a cancerous lesions. Unfortunately, for everyone involved in this work, including me, this scientific effort was interrupted by financial constrains before reaching maturity.

My short introduction was made to explain why I find the publication below important enough to post and bring to your attention.

I hope for your agreement on the matter.

Quantitative Imaging in Cancer Evolution and Ecology

Robert A. Gatenby, MD, Olya Grove, PhD and Robert J. Gillies, PhD

From the Departments of Radiology and Cancer Imaging and Metabolism, Moffitt Cancer Center, 12902 Magnolia Dr, Tampa, FL 33612. Address correspondence to  R.A.G. (e-mail:


Cancer therapy, even when highly targeted, typically fails because of the remarkable capacity of malignant cells to evolve effective adaptations. These evolutionary dynamics are both a cause and a consequence of cancer system heterogeneity at many scales, ranging from genetic properties of individual cells to large-scale imaging features. Tumors of the same organ and cell type can have remarkably diverse appearances in different patients. Furthermore, even within a single tumor, marked variations in imaging features, such as necrosis or contrast enhancement, are common. Similar spatial variations recently have been reported in genetic profiles. Radiologic heterogeneity within tumors is usually governed by variations in blood flow, whereas genetic heterogeneity is typically ascribed to random mutations. However, evolution within tumors, as in all living systems, is subject to Darwinian principles; thus, it is governed by predictable and reproducible interactions between environmental selection forces and cell phenotype (not genotype). This link between regional variations in environmental properties and cellular adaptive strategies may permit clinical imaging to be used to assess and monitor intratumoral evolution in individual patients. This approach is enabled by new methods that extract, report, and analyze quantitative, reproducible, and mineable clinical imaging data. However, most current quantitative metrics lack spatialness, expressing quantitative radiologic features as a single value for a region of interest encompassing the whole tumor. In contrast, spatially explicit image analysis recognizes that tumors are heterogeneous but not well mixed and defines regionally distinct habitats, some of which appear to harbor tumor populations that are more aggressive and less treatable than others. By identifying regional variations in key environmental selection forces and evidence of cellular adaptation, clinical imaging can enable us to define intratumoral Darwinian dynamics before and during therapy. Advances in image analysis will place clinical imaging in an increasingly central role in the development of evolution-based patient-specific cancer therapy.

© RSNA, 2013



Cancers are heterogeneous across a wide range of temporal and spatial scales. Morphologic heterogeneity between and within cancers is readily apparent in clinical imaging, and subjective descriptors of these differences, such as necrotic, spiculated, and enhancing, are common in the radiology lexicon. In the past several years, radiology research has increasingly focused on quantifying these imaging variations in an effort to understand their clinical and biologic implications (1,2). In parallel, technical advances now permit extensive molecular characterization of tumor cells in individual patients. This has led to increasing emphasis on personalized cancer therapy, in which treatment is based on the presence of specific molecular targets (3). However, recent studies (4,5) have shown that multiple genetic subpopulations coexist within cancers, reflecting extensive intratumoral somatic evolution. This heterogeneity is a clear barrier to therapy based on molecular targets, since the identified targets do not always represent the entire population of tumor cells in a patient (6,7). It is ironic that cancer, a disease extensively and primarily analyzed genetically, is also the most genetically flexible of all diseases and, therefore, least amenable to such an approach.

Genetic variations in tumors are typically ascribed to a mutator phenotype that generates new clones, some of which expand into large populations (8). However, although identification of genotypes is of substantial interest, it is insufficient for complete characterization of tumor dynamics because evolution is governed by the interactions of environmental selection forces with the phenotypic, not genotypic, properties of populations as shown, for example, by evolutionary convergence to identical phenotypes among cave fish even when they are from different species (911). This connection between tissue selection forces and cellular properties has the potential to provide a strong bridge between medical imaging and the cellular and molecular properties of cancers.

We postulate that differences within tumors at different spatial scales (ie, at the radiologic, cellular, and molecular [genetic] levels) are related. Tumor characteristics observable at clinical imaging reflect molecular-, cellular-, and tissue-level dynamics; thus, they may be useful in understanding the underlying evolving biology in individual patients. A challenge is that such mapping across spatial and temporal scales requires not only objective reproducible metrics for imaging features but also a theoretical construct that bridges those scales (Fig 1).


Figure 1a: Computed tomographic (CT) scan of right upper lobe lung cancer in a 50-year-old woman.


Figure 1b: Isoattenuation map shows regional heterogeneity at the tissue scale (measured in centimeters).


Figure 1c & 1d: (c, d)Whole-slide digital images (original magnification, ×3) of a histologic slice of the same tumor at the mesoscopic scale (measured in millimeters) (c) coupled with a masked image of regional morphologic differences showing spatial heterogeneity (d). 


Figure 1e: Subsegment of the whole slide image shows the microscopic scale (measured in micrometers) (original magnification, ×50).


Figure 1f: Pattern recognition masked image shows regional heterogeneity. In a, the CT image of non–small cell lung cancer can be analyzed to display gradients of attenuation, which reveals heterogeneous and spatially distinct environments (b). Histologic images in the same patient (c, e) reveal heterogeneities in tissue structure and density on the same scale as seen in the CT images. These images can be analyzed at much higher definition to identify differences in morphologies of individual cells (3), and these analyses reveal clusters of cells with similar morphologic features (d, f). An important goal of radiomics is to bridge radiologic data with cellular and molecular characteristics observed microscopically.

To promote the development and implementation of quantitative imaging methods, protocols, and software tools, the National Cancer Institute has established the Quantitative Imaging Network. One goal of this program is to identify reproducible quantifiable imaging features of tumors that will permit data mining and explicit examination of links between the imaging findings and the underlying molecular and cellular characteristics of the tumors. In the quest for more personalized cancer treatments, these quantitative radiologic features potentially represent nondestructive temporally and spatially variable predictive and prognostic biomarkers that readily can be obtained in each patient before, during, and after therapy.

Quantitative imaging requires computational technologies that can be used to reliably extract mineable data from radiographic images. This feature information can then be correlated with molecular and cellular properties by using bioinformatics methods. Most existing methods are agnostic and focus on statistical descriptions of existing data, without presupposing the existence of specific relationships. Although this is a valid approach, a more profound understanding of quantitative imaging information may be obtained with a theoretical hypothesis-driven framework. Such models use links between observable tumor characteristics and microenvironmental selection factors to make testable predictions about emergent phenotypes. One such theoretical framework is the developing paradigm of cancer as an ecologic and evolutionary process.

For decades, landscape ecologists have studied the effects of heterogeneity in physical features on interactions between populations of organisms and their environments, often by using observation and quantification of images at various scales (1214). We propose that analytic models of this type can easily be applied to radiologic studies of cancer to uncover underlying molecular, cellular, and microenvironmental drivers of tumor behavior and specifically, tumor adaptations and responses to therapy (15).

In this article, we review recent developments in quantitative imaging metrics and discuss how they correlate with underlying genetic data and clinical outcomes. We then introduce the concept of using ecology and evolutionary models for spatially explicit image analysis as an exciting potential avenue of investigation.


Quantitative Imaging and Radiomics

In patients with cancer, quantitative measurements are commonly limited to measurement of tumor size with one-dimensional (Response Evaluation Criteria in Solid Tumors [or RECIST]) or two-dimensional (World Health Organization) long-axis measurements (16). These measures do not reflect the complexity of tumor morphology or behavior, and in many cases, changes in these measures are not predictive of therapeutic benefit (17). In contrast, radiomics (18) is a high-throughput process in which a large number of shape, edge, and texture imaging features are extracted, quantified, and stored in databases in an objective, reproducible, and mineable form (Figs 12). Once transformed into a quantitative form, radiologic tumor properties can be linked to underlying genetic alterations (the field is called radiogenomics) (1921) and to medical outcomes (2227). Researchers are currently working to develop both a standardized lexicon to describe tumor features (28,29) and a standard method to convert these descriptors into quantitative mineable data (30,31) (Fig 3).


Figure 2: Contrast-enhanced CT scans show non–small cell lung cancer (left) and corresponding cluster map (right). Subregions within the tumor are identified by clustering pixels based on the attenuation of pixels and their cumulative standard deviation across the region. While the entire region of interest of the tumor, lacking the spatial information, yields a weighted mean attenuation of 859.5 HU with a large and skewed standard deviation of 243.64 HU, the identified subregions have vastly different statistics. Mean attenuation was 438.9 HU ± 45 in the blue subregion, 210.91 HU ± 79 in the yellow subregion, and 1077.6 HU ± 18 in the red subregion.



Figure 3: Chart shows the five processes in radiomics.

Several recent articles underscore the potential power of feature analysis. After manually extracting more than 100 CT image features, Segal and colleagues found that a subset of 14 features predicted 80% of the gene expression pattern in patients with hepatocellular carcinoma (21). A similar extraction of features from contrast agent–enhanced magnetic resonance (MR) images of glioblastoma was used to predict immunohistochemically identified protein expression patterns (22). Other radiomic features, such as texture, can be used to predict response to therapy in patients with renal cancer (32) and prognosis in those with metastatic colon cancer (33).

These pioneering studies were relatively small because the image analysis was performed manually, and the studies were consequently underpowered. Thus, recent work in radiomics has focused on technical developments that permit automated extraction of image features with the potential for high throughput. Such methods, which rely heavily on novel machine learning algorithms, can more completely cover the range of quantitative features that can describe tumor heterogeneity, such as texture, shape, or margin gradients or, importantly, different environments, or niches, within the tumors.

Generally speaking, texture in a biomedical image is quantified by identifying repeating patterns. Texture analyses fall into two broad categories based on the concepts of first- and second-order spatial statistics. First-order statistics are computed by using individual pixel values, and no relationships between neighboring pixels are assumed or evaluated. Texture analysis methods based on first-order statistics usually involve calculating cumulative statistics of pixel values and their histograms across the region of interest. Second-order statistics, on the other hand, are used to evaluate the likelihood of observing spatially correlated pixels (34). Hence, second-order texture analyses focus on the detection and quantification of nonrandom distributions of pixels throughout the region of interest.

The technical developments that permit second-order texture analysis in tumors by using regional enhancement patterns on dynamic contrast-enhanced MR images were reviewed recently (35). One such technique that is used to measure heterogeneity of contrast enhancement uses the Factor Analysis of Medical Image Sequences (or FAMIS) algorithm, which divides tumors into regions based on their patterns of enhancement (36). Factor Analysis of Medical Image Sequences–based analyses yielded better prognostic information when compared with region of interest–based methods in numerous cancer types (1921,3739), and they were a precursor to the Food and Drug Administration–approved three-time-point method (40). A number of additional promising methods have been developed. Rose and colleagues showed that a structured fractal-based approach to texture analysis improved differentiation between low- and high-grade brain cancers by orders of magnitude (41). Ahmed and colleagues used gray level co-occurrence matrix analyses of dynamic contrast-enhanced images to distinguish benign from malignant breast masses with high diagnostic accuracy (area under the receiver operating characteristic curve, 0.92) (26). Others have shown that Minkowski functional structured methods that convolve images with differently kernelled masks can be used to distinguish subtle differences in contrast enhancement patterns and can enable significant differentiation between treatment groups (42).

It is not surprising that analyses of heterogeneity in enhancement patterns can improve diagnosis and prognosis, as this heterogeneity is fundamentally based on perfusion deficits, which generate significant microenvironmental selection pressures. However, texture analysis is not limited to enhancement patterns. For example, measures of heterogeneity in diffusion-weighted MR images can reveal differences in cellular density in tumors, which can be matched to histologic findings (43). Measures of heterogeneity in T1- and T2-weighted images can be used to distinguish benign from malignant soft-tissue masses (23). CT-based texture features have been shown to be highly significant independent predictors of survival in patients with non–small cell lung cancer (24).

Texture analyses can also be applied to positron emission tomographic (PET) data, where they can provide information about metabolic heterogeneity (25,26). In a recent study, Nair and colleagues identified 14 quantitative PET imaging features that correlated with gene expression (19). This led to an association of metagene clusters to imaging features and yielded prognostic models with hazard ratios near 6. In a study of esophageal cancer, in which 38 quantitative features describing fluorodeoxyglucose uptake were extracted, measures of metabolic heterogeneity at baseline enabled prediction of response with significantly higher sensitivity than any whole region of interest standardized uptake value measurement (22). It is also notable that these extensive texture-based features are generally more reproducible than simple measures of the standardized uptake value (27), which can be highly variable in a clinical setting (44).


Spatially Explicit Analysis of Tumor Heterogeneity

Although radiomic analyses have shown high prognostic power, they are not inherently spatially explicit. Quantitative border, shape, and texture features are typically generated over a region of interest that comprises the entire tumor (45). This approach implicitly assumes that tumors are heterogeneous but well mixed. However, spatially explicit subregions of cancers are readily apparent on contrast-enhanced MR or CT images, as perfusion can vary markedly within the tumor, even over short distances, with changes in tumor cell density and necrosis.

An example is shown in Figure 2, which shows a contrast-enhanced CT scan of non–small cell lung cancer. Note that there are many subregions within this tumor that can be identified with attenuation gradient (attenuation per centimeter) edge detection algorithms. Each subregion has a characteristic quantitative attenuation, with a narrow standard deviation, whereas the mean attenuation over the entire region of interest is a weighted average of the values across all subregions, with a correspondingly large and skewed distribution. We contend that these subregions represent distinct habitats within the tumor, each with a distinct set of environmental selection forces.

These observations, along with the recent identification of regional variations in the genetic properties of tumor cells, indicate the need to abandon the conceptual model of cancers as bounded organlike structures. Rather than a single self-organized system, cancers represent a patchwork of habitats, each with a unique set of environmental selection forces and cellular evolution strategies. For example, regions of the tumor that are poorly perfused can be populated by only those cells that are well adapted to low-oxygen, low-glucose, and high-acid environmental conditions. Such adaptive responses to regional heterogeneity result in microenvironmental selection and hence, emergence of genetic variations within tumors. The concept of adaptive response is an important departure from the traditional view that genetic heterogeneity is the product of increased random mutations, which implies that molecular heterogeneity is fundamentally unpredictable and, therefore, chaotic. The Darwinian model proposes that genetic heterogeneity is the result of a predictable and reproducible selection of successful adaptive strategies to local microenvironmental conditions.

Current cross-sectional imaging modalities can be used to identify regional variations in selection forces by using contrast-enhanced, cell density–based, or metabolic features. Clinical imaging can also be used to identify evidence of cellular adaptation. For example, if a region of low perfusion on a contrast-enhanced study is necrotic, then an adaptive population is absent or minimal. However, if the poorly perfused area is cellular, then there is presumptive evidence of an adapted proliferating population. While the specific genetic properties of this population cannot be determined, the phenotype of the adaptive strategy is predictable since the environmental conditions are more or less known. Thus, standard medical images can be used to infer specific emergent phenotypes and, with ongoing research, these phenotypes can be associated with underlying genetic changes.

This area of investigation will likely be challenging. As noted earlier, the most obvious spatially heterogeneous imaging feature in tumors is perfusion heterogeneity on contrast-enhanced CT or MR images. It generally has been assumed that the links between contrast enhancement, blood flow, perfusion, and tumor cell characteristics are straightforward. That is, tumor regions with decreased blood flow will exhibit low perfusion, low cell density, and high necrosis. In reality, however, the dynamics are actually much more complex. As shown in Figure 4, when using multiple superimposed sequences from MR imaging of malignant gliomas, regions of tumor that are poorly perfused on contrast-enhanced T1-weighted images may exhibit areas of low or high water content on T2-weighted images and low or high diffusion on diffusion-weighted images. Thus, high or low cell densities can coexist in poorly perfused volumes, creating perfusion-diffusion mismatches. Regions with poor perfusion with high cell density are of particular clinical interest because they represent a cell population that is apparently adapted to microenvironmental conditions associated with poor perfusion. The associated hypoxia, acidosis, and nutrient deprivation select for cells that are resistant to apoptosis and thus are likely to be resistant to therapy (46,47).


Figure 4: Left: Contrast-enhanced T1 image from subject TCGA-02-0034 in The Cancer Genome Atlas–Glioblastoma Multiforme repository of MR volumes of glioblastoma multiforme cases. Right: Spatial distribution of MR imaging–defined habitats within the tumor. The blue region (low T1 postgadolinium, low fluid-attenuated inversion recovery) is particularly notable because it presumably represents a habitat with low blood flow but high cell density, indicating a population presumably adapted to hypoxic acidic conditions.

Furthermore, other selection forces not related to perfusion are likely to be present within tumors. For example, evolutionary models suggest that cancer cells, even in stable microenvironments, tend to speciate into “engineers” that maximize tumor cell growth by promoting angiogenesis and “pioneers” that proliferate by invading normal issue and co-opting the blood supply. These invasive tumor phenotypes can exist only at the tumor edge, where movement into a normal tissue microenvironment can be rewarded by increased proliferation. This evolutionary dynamic may contribute to distinct differences between the tumor edges and the tumor cores, which frequently can be seen at analysis of cross-sectional images (Fig 5).


Figure 5a: CT images obtained with conventional entropy filtering in two patients with non–small cell lung cancer with no apparent textural differences show similar entropy values across all sections. 


Figure 5b: Contour plots obtained after the CT scans were convolved with the entropy filter. Further subdividing each section in the tumor stack into tumor edge and core regions (dotted black contour) reveals varying textural behavior across sections. Two distinct patterns have emerged, and preliminary analysis shows that the change of mean entropy value between core and edge regions correlates negatively with survival.

Interpretation of the subsegmentation of tumors will require computational models to understand and predict the complex nonlinear dynamics that lead to heterogeneous combinations of radiographic features. We have exploited ecologic methods and models to investigate regional variations in cancer environmental and cellular properties that lead to specific imaging characteristics. Conceptually, this approach assumes that regional variations in tumors can be viewed as a coalition of distinct ecologic communities or habitats of cells in which the environment is governed, at least to first order, by variations in vascular density and blood flow. The environmental conditions that result from alterations in blood flow, such as hypoxia, acidosis, immune response, growth factors, and glucose, represent evolutionary selection forces that give rise to local-regional phenotypic adaptations. Phenotypic alterations can result from epigenetic, genetic, or chromosomal rearrangements, and these in turn will affect prognosis and response to therapy. Changes in habitats or the relative abundance of specific ecologic communities over time and in response to therapy may be a valuable metric with which to measure treatment efficacy and emergence of resistant populations.


Emerging Strategies for Tumor Habitat Characterization

A method for converting images to spatially explicit tumor habitats is shown in Figure 4. Here, three-dimensional MR imaging data sets from a glioblastoma are segmented. Each voxel in the tumor is defined by a scale that includes its image intensity in different sequences. In this case, the imaging sets are from (a) a contrast-enhanced T1 sequence, (b) a fast spin-echo T2 sequence, and (c) a fluid-attenuated inversion-recovery (or FLAIR) sequence. Voxels in each sequence can be defined as high or low based on their value compared with the mean signal value. By using just two sequences, a contrast-enhanced T1 sequence and a fluid-attenuated inversion-recovery sequence, we can define four habitats: high or low postgadolinium T1 divided into high or low fluid-attenuated inversion recovery. When these voxel habitats are projected into the tumor volume, we find they cluster into spatially distinct regions. These habitats can be evaluated both in terms of their relative contributions to the total tumor volume and in terms of their interactions with each other, based on the imaging characteristics at the interfaces between regions. Similar spatially explicit analysis can be performed with CT scans (Fig 5).

Analysis of spatial patterns in cross-sectional images will ultimately require methods that bridge spatial scales from microns to millimeters. One possible method is a general class of numeric tools that is already widely used in terrestrial and marine ecology research to link species occurrence or abundance with environmental parameters. Species distribution models (4851) are used to gain ecologic and evolutionary insights and to predict distributions of species or morphs across landscapes, sometimes extrapolating in space and time. They can easily be used to link the environmental selection forces in MR imaging-defined habitats to the evolutionary dynamics of cancer cells.


Imaging can have an enormous role in the development and implementation of patient-specific therapies in cancer. The achievement of this goal will require new methods that expand and ultimately replace the current subjective qualitative assessments of tumor characteristics. The need for quantitative imaging has been clearly recognized by the National Cancer Institute and has resulted in formation of the Quantitative Imaging Network. A critical objective of this imaging consortium is to use objective, reproducible, and quantitative feature metrics extracted from clinical images to develop patient-specific imaging-based prognostic models and personalized cancer therapies.

It is increasingly clear that tumors are not homogeneous organlike systems. Rather, they contain regional coalitions of ecologic communities that consist of evolving cancer, stroma, and immune cell populations. The clinical consequence of such niche variations is that spatial and temporal variations of tumor phenotypes will inevitably evolve and present substantial challenges to targeted therapies. Hence, future research in cancer imaging will likely focus on spatially explicit analysis of tumor regions.

Clinical imaging can readily characterize regional variations in blood flow, cell density, and necrosis. When viewed in a Darwinian evolutionary context, these features reflect regional variations in environmental selection forces and can, at least in principle, be used to predict the likely adaptive strategies of the local cancer population. Hence, analyses of radiologic data can be used to inform evolutionary models and then can be mapped to regional population dynamics. Ecologic and evolutionary principles may provide a theoretical framework to link imaging to the cellular and molecular features of cancer cells and ultimately lead to a more comprehensive understanding of specific cancer biology in individual patients.



  • • Marked heterogeneity in genetic properties of different cells in the same tumor is typical and reflects ongoing intratumoral evolution.
  • • Evolution within tumors is governed by Darwinian dynamics, with identifiable environmental selection forces that interact with phenotypic (not genotypic) properties of tumor cells in a predictable and reproducible manner; clinical imaging is uniquely suited to measure temporal and spatial heterogeneity within tumors that is both a cause and a consequence of this evolution.
  • • Subjective radiologic descriptors of cancers are inadequate to capture this heterogeneity and must be replaced by quantitative metrics that enable statistical comparisons between features describing intratumoral heterogeneity and clinical outcomes and molecular properties.
  • • Spatially explicit mapping of tumor regions, for example by superimposing multiple imaging sequences, may permit patient-specific characterization of intratumoral evolution and ecology, leading to patient- and tumor-specific therapies.
  • • We summarize current information on quantitative analysis of radiologic images and propose future quantitative imaging must become spatially explicit to identify intratumoral habitats before and during therapy.

Disclosures of Conflicts of Interest: R.A.G. No relevant conflicts of interest to disclose. O.G. No relevant conflicts of interest to disclose.R.J.G. No relevant conflicts of interest to disclose.



The authors thank Mark Lloyd, MS; Joel Brown, PhD; Dmitry Goldgoff, PhD; and Larry Hall, PhD, for their input to image analysis and for their lively and informative discussions.


  • Received December 18, 2012; revision requested February 5, 2013; revision received March 11; accepted April 9; final version accepted April 29.
  • Funding: This research was supported by the National Institutes of Health (grants U54CA143970-01, U01CA143062; R01CA077575, andR01CA170595).


    1. Kurland BF,
    2. Gerstner ER,
    3. Mountz JM,
    4. et al

    . Promise and pitfalls of quantitative imaging in oncology clinical trials. Magn Reson Imaging2012;30(9):1301–1312.

    1. Levy MA,
    2. Freymann JB,
    3. Kirby JS,
    4. et al

    . Informatics methods to enable sharing of quantitative imaging research data. Magn Reson Imaging2012;30(9):1249–1256.

    1. Mirnezami R,
    2. Nicholson J,
    3. Darzi A

    . Preparing for precision medicine. N Engl J Med 2012;366(6):489–491.

    1. Yachida S,
    2. Jones S,
    3. Bozic I,
    4. et al

    . Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 2010;467(7319):1114–1117.

    1. Gerlinger M,
    2. Rowan AJ,
    3. Horswell S,
    4. et al

    . Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med2012;366(10):883–892.

    1. Gerlinger M,
    2. Swanton C

    . How Darwinian models inform therapeutic failure initiated by clonal heterogeneity in cancer medicine. Br J Cancer2010;103(8):1139–1143.

    1. Kern SE

    . Why your new cancer biomarker may never work: recurrent patterns and remarkable diversity in biomarker failures. Cancer Res2012;72(23):6097–6101.

    1. Nowell PC

    . The clonal evolution of tumor cell populations. Science1976;194(4260):23–28.

    1. Greaves M,
    2. Maley CC

    . Clonal evolution in cancer. Nature2012;481(7381):306–313.

    1. Vincent TL,
    2. Brown JS

    . Evolutionary game theory, natural selection and Darwinian dynamics. Cambridge, England: Cambridge University Press, 2005.

    1. Gatenby RA,
    2. Gillies RJ

    . A microenvironmental model of carcinogenesis. Nat Rev Cancer 2008;8(1):56–61.

    1. Bowers MA,
    2. Matter SF

    . Landscape ecology of mammals: relationships between density and patch size. J Mammal 1997;78(4):999–1013.

    1. Dorner BK,
    2. Lertzman KP,
    3. Fall J

    . Landscape pattern in topographically complex landscapes: issues and techniques for analysis. Landscape Ecol2002;17(8):729–743.

    1. González-García I,
    2. Solé RV,
    3. Costa J

    . Metapopulation dynamics and spatial heterogeneity in cancer. Proc Natl Acad Sci U S A2002;99(20):13085–13089.

    1. Patel LR,
    2. Nykter M,
    3. Chen K,
    4. Zhang W

    . Cancer genome sequencing: understanding malignancy as a disease of the genome, its conformation, and its evolution. Cancer Lett 2012 Oct 27. [Epub ahead of print]

    1. Jaffe CC

    . Measures of response: RECIST, WHO, and new alternatives. J Clin Oncol 2006;24(20):3245–3251.

    1. Burton A

    . RECIST: right time to renovate? Lancet Oncol2007;8(6):464–465.

    1. Lambin P,
    2. Rios-Velazquez E,
    3. Leijenaar R,
    4. et al

    . Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012;48(4):441–446.

    1. Nair VS,
    2. Gevaert O,
    3. Davidzon G,
    4. et al

    . Prognostic PET 18F-FDG uptake imaging features are associated with major oncogenomic alterations in patients with resected non-small cell lung cancer. Cancer Res2012;72(15):3725–3734.

    1. Diehn M,
    2. Nardini C,
    3. Wang DS,
    4. et al

    . Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci U S A 2008;105(13):5213–5218.

    1. Segal E,
    2. Sirlin CB,
    3. Ooi C,
    4. et al

    . Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 2007;25(6):675–680.

    1. Tixier F,
    2. Le Rest CC,
    3. Hatt M,
    4. et al

    . Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med2011;52(3):369–378.

    1. Pang KK,
    2. Hughes T

    . MR imaging of the musculoskeletal soft tissue mass: is heterogeneity a sign of malignancy? J Chin Med Assoc2003;66(11):655–661.

    1. Ganeshan B,
    2. Panayiotou E,
    3. Burnand K,
    4. Dizdarevic S,
    5. Miles K

    . Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol 2012;22(4):796–802.

    1. Asselin MC,
    2. O’Connor JP,
    3. Boellaard R,
    4. Thacker NA,
    5. Jackson A

    . Quantifying heterogeneity in human tumours using MRI and PET. Eur J Cancer2012;48(4):447–455.

    1. Ahmed A,
    2. Gibbs P,
    3. Pickles M,
    4. Turnbull L

    . Texture analysis in assessment and prediction of chemotherapy response in breast cancer. J Magn Reson Imaging doi:10.1002/jmri.23971 2012. Published online December 13, 2012.

    1. Kawata Y,
    2. Niki N,
    3. Ohmatsu H,
    4. et al

    . Quantitative classification based on CT histogram analysis of non-small cell lung cancer: correlation with histopathological characteristics and recurrence-free survival. Med Phys2012;39(2):988–1000.

    1. Rubin DL

    . Creating and curating a terminology for radiology: ontology modeling and analysis. J Digit Imaging 2008;21(4):355–362.

    1. Opulencia P,
    2. Channin DS,
    3. Raicu DS,
    4. Furst JD

    . Mapping LIDC, RadLex™, and lung nodule image features. J Digit Imaging 2011;24(2):256–270.

    1. Channin DS,
    2. Mongkolwat P,
    3. Kleper V,
    4. Rubin DL

    . The Annotation and Image Mark-up project. Radiology 2009;253(3):590–592.

    1. Rubin DL,
    2. Mongkolwat P,
    3. Kleper V,
    4. Supekar K,
    5. Channin DS

    . Medical imaging on the semantic web: annotation and image markup. Presented at the AAAI Spring Symposium Series, Semantic Scientific Knowledge Integration, Palo Alto, Calif, March 26–28, 2008.

    1. Goh V,
    2. Ganeshan B,
    3. Nathan P,
    4. Juttla JK,
    5. Vinayan A,
    6. Miles KA

    . Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. Radiology 2011;261(1):165–171.

    1. Miles KA,
    2. Ganeshan B,
    3. Griffiths MR,
    4. Young RC,
    5. Chatwin CR

    . Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology 2009;250(2):444–452.

    1. Haralick RM,
    2. Shanmugam K,
    3. Dinstein I

    . Textural features for image classification. IEEE Trans Syst Man Cybern 1973;3(6):610–621.

    1. Yang X,
    2. Knopp MV

    . Quantifying tumor vascular heterogeneity with dynamic contrast-enhanced magnetic resonance imaging: a review. J Biomed Biotechnol 2011;2011:732848.

    1. Frouin F,
    2. Bazin JP,
    3. Di Paola M,
    4. Jolivet O,
    5. Di Paola R

    . FAMIS: a software package for functional feature extraction from biomedical multidimensional images. Comput Med Imaging Graph 1992;16(2):81–91.

    1. Frouge C,
    2. Guinebretière JM,
    3. Contesso G,
    4. Di Paola R,
    5. Bléry M

    . Correlation between contrast enhancement in dynamic magnetic resonance imaging of the breast and tumor angiogenesis. Invest Radiol 1994;29(12):1043–1049.

    1. Zagdanski AM,
    2. Sigal R,
    3. Bosq J,
    4. Bazin JP,
    5. Vanel D,
    6. Di Paola R

    . Factor analysis of medical image sequences in MR of head and neck tumors. AJNR Am J Neuroradiol 1994;15(7):1359–1368.

    1. Bonnerot V,
    2. Charpentier A,
    3. Frouin F,
    4. Kalifa C,
    5. Vanel D,
    6. Di Paola R

    . Factor analysis of dynamic magnetic resonance imaging in predicting the response of osteosarcoma to chemotherapy. Invest Radiol 1992;27(10):847–855.

    1. Furman-Haran E,
    2. Grobgeld D,
    3. Kelcz F,
    4. Degani H

    . Critical role of spatial resolution in dynamic contrast-enhanced breast MRI. J Magn Reson Imaging2001;13(6):862–867.

    1. Rose CJ,
    2. Mills SJ,
    3. O’Connor JPB,
    4. et al

    . Quantifying spatial heterogeneity in dynamic contrast-enhanced MRI parameter maps. Magn Reson Med2009;62(2):488–499.

    1. Canuto HC,
    2. McLachlan C,
    3. Kettunen MI,
    4. et al

    . Characterization of image heterogeneity using 2D Minkowski functionals increases the sensitivity of detection of a targeted MRI contrast agent. Magn Reson Med2009;61(5):1218–1224.

    1. Lloyd MC,
    2. Allam-Nandyala P,
    3. Purohit CN,
    4. Burke N,
    5. Coppola D,
    6. Bui MM

    . Using image analysis as a tool for assessment of prognostic and predictive biomarkers for breast cancer: how reliable is it? J Pathol Inform2010;1:29–36.

    1. Kumar V,
    2. Nath K,
    3. Berman CG,
    4. et al

    . Variance of SUVs for FDG-PET/CT is greater in clinical practice than under ideal study settings. Clin Nucl Med2013;38(3):175–182.

    1. Walker-Samuel S,
    2. Orton M,
    3. Boult JK,
    4. Robinson SP

    . Improving apparent diffusion coefficient estimates and elucidating tumor heterogeneity using Bayesian adaptive smoothing. Magn Reson Med 2011;65(2):438–447.

    1. Thews O,
    2. Nowak M,
    3. Sauvant C,
    4. Gekle M

    . Hypoxia-induced extracellular acidosis increases p-glycoprotein activity and chemoresistance in tumors in vivo via p38 signaling pathway. Adv Exp Med Biol 2011;701:115–122.

    1. Thews O,
    2. Dillenburg W,
    3. Rösch F,
    4. Fellner M

    . PET imaging of the impact of extracellular pH and MAP kinases on the p-glycoprotein (Pgp) activity. Adv Exp Med Biol 2013;765:279–286.

    1. Araújo MB,
    2. Peterson AT

    . Uses and misuses of bioclimatic envelope modeling. Ecology 2012;93(7):1527–1539.

    1. Larsen PE,
    2. Gibbons SM,
    3. Gilbert JA

    . Modeling microbial community structure and functional diversity across time and space. FEMS Microbiol Lett2012;332(2):91–98.

    1. Shenton W,
    2. Bond NR,
    3. Yen JD,
    4. Mac Nally R

    . Putting the “ecology” into environmental flows: ecological dynamics and demographic modelling. Environ Manage 2012;50(1):1–10.

    1. Clark MC,
    2. Hall LO,
    3. Goldgof DB,
    4. Velthuizen R,
    5. Murtagh FR,
    6. Silbiger MS

    .Automatic tumor segmentation using knowledge-based techniques. IEEE Trans Med Imaging 1998;17(2):187–201.

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Recent comprehensive review on the role of ultrasound in breast cancer management

Writer, reporter and curator: Dror Nir, PhD

The paper below by R Hooley is a beautifully written review on how ultrasound could (and should) be practiced to better support breast cancer screening, staging, and treatment. The authors went as well into the effort of describing the benefits from combining ultrasonography with the other frequently used imaging modalities; i.e. mammography, tomosynthesis and MRI. Post treatment use of ultrasound is not discussed although this is a major task for this modality.

I would like to recommend giving attention to two very small (but for me very important) paragraphs: “Speed of Sound Imaging” and “Lesion Annotation”


Breast Ultrasonography: State of the Art

Regina J. Hooley, MDLeslie M. Scoutt, MD and Liane E. Philpotts, MD

Department of Diagnostic Radiology, Yale University School of Medicine, 333 Cedar St, PO Box 208042, New Haven, CT 06520-8042.

Address correspondence to R.J.H. (e-mail:

Ultrasonography (US) has become an indispensable tool in breast imaging. Breast US was first introduced in the 1950s by using radar techniques adapted from the U.S. Navy (1). Over the next several decades, US in breast imaging was primarily used to distinguish cystic from solid masses. This was clinically important, as a simple breast cyst is a benign finding that does not require further work-up. However, most solid breast lesions remained indeterminate and required biopsy, as US was not adequately specific in differentiating benign from malignant solid breast masses. However, recent advances in US technology have allowed improved characterization of solid masses.

In 1995, Stavros et al (2) published a landmark study demonstrating that solid breast lesions could be confidently characterized as benign or malignant by using high-resolution grays-cale US imaging. Benign US features include few (two or three) gentle lobulations, ellipsoid shape, and a thin capsule, as well as a homogeneously echogenic echotexture. Malignant US features include spiculation, taller-than-wide orientation, angular margins, microcalcifications, and posterior acoustic shadowing. With these sonographic features, a negative predictive value of 99.5% and a sensitivity of 98.4% for the diagnosis of malignancy were achieved. These results have subsequently been validated by others (3,4) and remain the cornerstone of US characterization of breast lesions today. These features are essential in the comprehensive US assessment of breast lesions, described by the Breast Imaging and Reporting Data System (BI-RADS) (5).

US is both an adjunct and a complement to mammography. Advances in US technology include harmonic imaging, compound imaging, power Doppler, faster frame rates, higher resolution transducers, and, more recently, elastography and three-dimensional (3D) US. Currently accepted clinical indications include evaluation of palpable abnormalities and characterization of masses detected at mammography and magnetic resonance (MR) imaging. US may also be used as an adjuvant breast cancer screening modality in women with dense breast tissue and a negative mammogram. These applications of breast US have broadened the spectrum of sonographic features currently assessed, even allowing detection of noninvasive disease, a huge advance beyond the early simplistic cyst-versus-solid assessment. In addition, US is currently the primary imaging modality recommended to guide interventional breast procedures.

The most subtle US features of breast cancers are likely to be best detected by physicians who routinely synthesize findings from multiple imaging modalities and clinical information, as well as perform targeted US to correlate with lesions detected at mammography or MR imaging. Having a strong understanding of the technical applications of US and image optimization, in addition to strong interpretive and interventional US skills, is essential for today’s breast imager.


Optimal Imaging Technique

US is operator dependent, and meticulous attention to scanning technique as well as knowledge of the various technical options available are imperative for an optimized and accurate breast US examination. US is an interactive, dynamic modality. Although breast US scanning may be performed by a sonographer or mammography technologist, the radiologist also benefits greatly from hands-on scanning (Fig 1). Berg et al (6) demonstrated that US interpretive performance was improved if the radiologist had direct experience performing breast US scanning, including rescanning after the technologist. Real-time scanning also provides the opportunity for thorough evaluation of lesions and permits detailed lesion analysis compared with analyzing static images on a workstation. Subtle irregular or indistinct margins, artifacts, and architectural distortions may be difficult to capture on static images. Real-time scanning also allows the operator to assess lesion mobility, location, and relationship to adjacent structures and allows direct assessment of palpable lesions and other clinical findings. Moreover, careful review of any prior imaging studies is imperative to ensure accurate lesion correlation.



The US examination is generally well tolerated by the patient. Gentle but firm transducer pressure and optimal patient positioning are essential, with the patient’s arm relaxed and flexed behind the head. Medial lesions should generally be scanned in the supine position, and lateral lesions, including the axilla, should usually be scanned with the patient in the contralateral oblique position. This allows for elimination of potential artifact secondary to inadequate compression of breast tissue.


Gray-Scale Imaging

Typical US transducers used in breast imaging today have between 192 and 256 elements along the long axis. When scanning the breast, a linear 12–5-MHz transducer is commonly used. However, in small-breasted women (with breast thickness < 3 cm) or when performing targeted US to evaluate a superficial lesion, a linear 17–5-MHz transducer may be used. Such high-frequency transducers provide superb spatial and soft-tissue resolution, permitting substantially improved differentiation of subtle shades of gray, margin resolution, and lesion conspicuity in the background of normal breast tissue (Fig 2). However, the cost of such a high insonating frequency is decreased penetration due to attenuation of the ultrasound beam, making visualization of deep posterior tissue difficult (ie, greater than 3 cm in depth by using a linear 17–5-MHz transducer or greater than 5 cm in depth by using a linear 12–5-MHz transducer).



During the initial US survey of the region of interest in the breast, the depth should be set so that the pectoralis muscle is visualized along the posterior margin of the field of view. Initial gain settings should be adjusted so that fat at all levels is displayed as a midlevel gray. Simple cysts are anechoic. Compared with breast fat, most solid masses are hypoechoic, while the skin, Cooper ligaments, and fibrous tissue are echogenic. Time gain compensation, which adjusts image brightness at different depths from the skin to compensate for attenuation of the ultrasound beam as it penetrates into the breast tissue, may be set manually or, with appropriate equipment, may be adjusted automatically during real-time scanning or even during postprocessing of the image.

When searching for a lesion initially identified at mammography or MR imaging, careful correlation with lesion depth and surrounding anatomic structures is imperative. Lesion location may be affected by the patient’s position, which differs during mammography, US, and MR imaging examinations. Attention to surrounding background tissue may assist in accurate lesion correlation across multiple modalities. If a mass identified at mammography or MR imaging is surrounded entirely by fat or fibroglandular tissue, at US it should also be surrounded by hypoechoic fat or echogenic fibroglandular tissue, respectively. Similarly, careful attention to the region of clinical concern is necessary when scanning a palpable abnormality to ensure that the correct area is scanned. The examiner should place a finger on the palpable abnormality and then place the transducer directly over the region. Occasionally, the US examination may be performed in the sitting position if a breast mass can only be palpated when the patient is upright.

After a lesion is identified, or while searching for a subtle finding, the depth or field of view may be adjusted as needed. The depth should be decreased to better visualize more superficial structures or increased to better visualize deeper posterior lesions. The use of multiple focal zones also improves resolution at multiple depths simultaneously and should be used, if available. Although this reduces the frame rate, the reduction is typically negligible when scanning relatively superficial structures within the breast. If a single focal zone is selected to better evaluate a single lesion, the focal zone should be centered at the same level as the area of interest or minimally posterior to the area of interest, for optimal visualization.


Spatial Compounding, Speckle Reduction, and Harmonic Imaging

Spatial compound imaging and speckle reduction are available on most high-end US units and should be routinely utilized throughout the breast US examination. Unlike standard US imaging, in which ultrasound pulses are transmitted in a single direction perpendicular to the long axis of the transducer, spatial compounding utilizes electronic beam steering to acquire multiple images obtained from different angles within the plane of imaging (79). A single composite image is then obtained in real-time by averaging frames obtained by ultrasound beams acquired from these multiple angles (10). Artifactual echoes, including speckle and other spurious noise, as well as posterior acoustic patterns, including posterior enhancement (characteristic of simple cysts) and posterior acoustic shadowing (characteristic of some solid masses), are substantially reduced. However, returning echoes from real structures are enhanced, providing improved contrast resolution (9) so that ligaments, edge definition, and lesion margins, including spiculations, echogenic halos, posterior and lateral borders, as well as microcalcifications, are better defined. Speckle reduction is a real-time postprocessing technique that also enhances contrast resolution, improves border definition, is complementary to spatial compounding, and can be used simultaneously.

When a lesion is identified, harmonic imaging may also be applied—usually along with spatial compounding—to better characterize a cyst or a subtle solid mass. The simultaneous use of spatial compounding and harmonic imaging may decrease the frame rate, although this usually does not impair real-time evaluation. Harmonic imaging relies on filtering the multiple higher harmonic frequencies, which are multiples of the fundamental frequencies. All tissue is essentially nonlinear to sound propagation and the ultrasound pulse is distorted as it travels through breast tissue, creating harmonic frequencies (9). The returning ultrasound signal therefore contains both the original fundamental frequency and its multiples, or harmonics. Harmonic imaging allows the higher harmonic frequencies to be selected and used to create the gray-scale images (89). Lower-frequency superficial reverberation echoes are thereby reduced, allowing improved characterization of simple cysts (particularly if small) through the elimination of artifactual internal echoes often seen in fluid. Harmonic imaging also improves lateral resolution (10) and may also improve contrast between fatty tissue and subtle lesions, allowing better definition of subtle lesion margins and posterior shadowing (Fig 3).





Speed of Sound Imaging

Conventional US systems set the speed of sound in tissue at a uniform 1540 m/sec (10). However, the speed of sound in tissues of different composition is variable and this variability may compromise US image quality. Breast tissue usually contains fat, and the speed of sound in fat, of approximately 1430–1470 m/sec, is slower than the assumed standard (11). Accurate speed of sound imaging, in which the US transducer may be optimized for the presence of fat within breast tissue, has been shown to improve lateral resolution (12). Additionally, it can be used to better characterize tissue interfaces, lesion margins, and microcalcifications (13) and may also be useful to identify subtle hypoechoic lesions surrounded by fatty breast tissue. Speed of sound imaging is available on most high-end modern US units and is an optional adjustment, depending on whether predominately fatty, predominately dense, or mixed breast tissue is being scanned.


 Lesion Annotation

When a mass is identified and the US settings are optimized, the mass should be scanned with US “sweeps” through the entire lesion in multiple planes. Images of the lesion in the radial and antiradial views should be captured and annotated with “right” or “left,” clock face position, and centimeters from the nipple. Radial and anti-radial scanning planes are preferred over standard transverse and sagittal scanning planes because scanning the breast along the normal axis of the mammary ducts and lobar tissues allows improved understanding of the site of lesion origin and better visualization of ductal extension and helps narrow the differential diagnosis (14). Images should be captured with and without calipers to allow margin assessment on static images. Lesion size should be measured in three dimensions, reporting the longest horizontal diameter first, followed by the anteroposterior diameter, then the orthogonal horizontal.


 Extended-Field-of-View Imaging

Advanced US technology permits extended-field-of-view imaging beyond the footprint of the transducer. By using a freehand technique, the operator slides the transducer along the desired region to be imaged. The resultant images are stored in real-time and, by applying pattern recognition, a single large-field-of-view image is obtained (7). This can be helpful in measuring very large lesions as well as the distance between multiple structures in the breast and for assessing the relationship of multifocal disease (located in the same quadrant as the index cancer or within 4–5 cm of the index cancer, along the same duct system) and/or multicentric disease (located in a different quadrant than the index cancer, or at a distance greater than 4–5 cm, along a different duct system).


 Doppler US

Early studies investigating the use of color, power, and quantitative spectral Doppler US in the breast reported that the presence of increased vascularity, as well as changes in the pulsatility and resistive indexes, showed that these Doppler findings could be used to reliably characterize malignant lesions (15,16). However, other investigators have demonstrated substantial overlap of many of these Doppler characteristics in both benign and malignant breast lesions (17). Gokalp et al (18) also demonstrated that the addition of power Doppler US and spectral analysis to BI-RADS US features of solid breast masses did not improve specificity. While the current BI-RADS US lexicon recommends evaluation of lesion vascularity, it is not considered mandatory (5).

Power Doppler is generally more sensitive than color Doppler to low-flow volumes typical of breast lesions. Light transducer pressure is necessary to prevent occlusion of slow flow owing to compression of the vessel lumen. Currently both power and color Doppler are complementary tools to gray-scale imaging, and power Doppler may improve sensitivity in detecting malignant breast lesions (18,19). Demonstration of irregular branching central or penetrating vascularity within a solid mass raises suspicion of malignant neovascularity (20). Recently, the parallel artery and vein sign has been described as a reliable feature that has the potential to enable prediction of benignity in solid masses so that biopsy may be avoided. In a single study, a paired artery and vein was present in 13.2% of over 1000 masses at US-guided CNB and although an infrequent finding, the specificity for benignity was 99.3% and the false-negative rate was only 1.4%, with two malignancies among 142 masses in which the parallel artery and vein sign was identified (21).

Color and power Doppler US are also useful to evaluate cysts and complex cystic masses that contain a solid component. High-grade invasive cancer and metastatic lymph nodes may occasionally appear anechoic. Demonstration of flow within an otherwise simple appearing cyst, a complicated cyst, or a complex mass confirms the presence of a suspicious solid component, which requires biopsy. In addition, twinkle artifact seen with color Doppler US is useful to identify a biopsy marker clip or subtle echogenic microcalcifications (Fig 4). This Doppler color artifact occurs secondary to the presence of a strong reflecting granular surface and results in a rapidly changing mix of color adjacent to and behind the reflector (22). Care must be taken to avoid mistaking twinkle artifact for true vascular flow and, if in doubt, a spectral Doppler tracing can be obtained, as a normal vascular waveform will not be seen with a twinkle artifact.







At physical examination, it has long been recognized that malignant tumors tend to feel hard when compared with benign lesions. US elastography can be used to measure tissue stiffness with the potential to improve specificity in the diagnosis of breast masses. There are two forms of US elastography available today: strain and shear wave. With either technique, acoustic information regarding lesion stiffness is converted into a black-and-white or color-scaled image that can also be superimposed on top of a B-mode gray-scale image.

Strain elastography requires gentle compression with a US probe or natural motion (such as heart beat, vascular pulsation, or respiration) and results in tissue displacement, or strain. Strain (ie, tissue compression and motion) is decreased in hard tissues compared with soft tissue (23). The information obtained with strain elastography provides qualitative information, although strain ratios may be calculated by comparing the strain of a lesion to the surrounding normal tissue. Benign breast lesions generally have lower ratios in comparison to malignant lesions (24,25).

Shear-wave elastography is based on the principle of acoustic radiation force. With use of light transducer pressure, transient automatic pulses can be generated by the US probe, inducing transversely oriented shear waves in tissue. The US system captures the velocity of these shear waves, which travel faster in hard tissue compared with soft tissue (26). Shear-wave elastography provides quantitative information because the elasticity of the tissue can be measured in meters per second or in kilopascals, a unit of pressure.

Elastography features such as strain ratios, size ratios, shape, homogeneity, and maximum lesion stiffness may complement conventional US in the analysis of breast lesions. Malignant masses evaluated with elastography tend to be more irregular, heterogeneous, and typically appear larger at elastography than at grayscale imaging (Fig 5) (27,28). Although malignant lesions generally also exhibit maximum stiffness greater than 80–100 kPa (28,29), caution is necessary when applying these numerical values to lesion analysis. Berg et al (28) reported three cancers among 115 masses with maximum stiffness between 20 and 30 kPa, for a 2.6% malignancy rate; 25 cancers among 281 masses with maximum stiffness between 30 and 80 kPa, for an 8.9% malignancy rate; and 61 cancers among 153 masses with maximum stiffness between 80 and 160 kPa, for a 39.9% malignancy rate (28). Invasive cancers with high histologic grade, large tumor size, nodal involvement, and vascular invasion have also been shown to be significantly correlated with high mean stiffness at shear-wave elastography (30).



Elastography may be useful in improving the specificity of US evaluation of BI-RADS 3 and 4A lesions, including complicated cysts. Berg and colleagues (28) showed that by using qualitative shear-wave elastography and color assessment of lesion stiffness, oval shape, and a maximum elasticity value of less than 80 kPa, unnecessary biopsy of low-suspicion BI-RADS 4A masses could be reduced without a significant loss in sensitivity. Several investigators have proposed a variety of imaging classifications using strain elastography, mostly based on the color pattern (27,31,32). A “bull’s eye” artifact has also been described as a characteristic feature present in benign breast cysts, which may appear as a round or oval lesion with a stiff rim associated with two soft spots, one located centrally and the other posteriorly (33).

Despite these initial promising studies regarding the role of US elastography in the analysis of breast lesions, limitations do exist. Strain and shear-wave elastography are quite different methods of measuring breast tissue stiffness, and the application of these methods varies across different commercial manufacturers. Inter- and intraobserver variability may be relatively high because the elastogram may be affected by differences in degree and method of compression. With strain elastography, a quality indicator that is an associated color bar or numerical value may be helpful to ensure proper light compression. Shear-wave elastography has been shown to be less operator-dependent, as tissue compression is initiated by the US probe in a standard, reproducible fashion (34) and only light transducer pressure is necessary. In addition, there is currently no universal color-coding standard and, depending on the manufacturer and/or operator preference, stiff lesions may be arbitrarily coded to appear red while soft lesions appear blue, or vice versa. Some elastography features such as the “bull’s eye” artifact are only seen on specific US systems. Lesions deeper than 2 cm are less accurately characterized by means of elastography. Moreover, one must be aware that soft cancers and hard benign lesions exist. Therefore, careful correlation of elastography with B-mode US features and mammography is essential. Future studies and further technical advances, including the creation of more uniformity across different US manufacturers, will ultimately determine the usefulness of elastography in clinical practice.

Three-dimensional US

Both handheld and automated high-resolution linear 3D transducers are now available for use in breast imaging. With a single pass of the ultrasound beam, a 3D reconstructed image can be formed in the coronal, sagittal, and transverse planes, potentially allowing more accurate assessment of anatomic structures and tumor margins (Fig 6). Few studies regarding the performance of 3D US in the breast exist, but a preliminary study demonstrated improved characterization of malignant lesions (35). Automated supine whole-breast US using 3D technology is now widely available for use in the screening setting (see section on screening breast US). Three-dimensional US may also be used in addition to computed tomography for image-guided radiation therapy (36) and has a potential role in assessing tumor response to neoadjuvant chemotherapy.




US Features of Benign and Malignant Breast Lesions


Although for many years the main function of breast US was to differentiate cysts from solid masses, this differentiation can at times be problematic, particularly if the lesion is small or located deep in the breast. Simple cysts are defined as circumscribed, anechoic masses with a thin imperceptible wall and enhanced through transmission (provided spatial compounding is not used). By convention, simple cysts may also contain up to a single thin septation. Simple cysts are confidently characterized with virtually 100% accuracy at US (14,37), provided that they are not very small (< 5 mm in size) or not located in deep tissue. Complicated cysts are hypoechoic with no discernable Doppler flow, contain internal echoes, and may also exhibit indistinct margins, and/or lack posterior acoustic enhancement. Clustered microcysts consist of a cluster of tiny (<2–3 mm in size) anechoic foci with thin (< 0.5 mm in thickness) intervening septations.

Complicated cysts are very common sonographic findings and the majority are benign. In multiple studies, which evaluated over 1400 complicated cysts and microcysts, the malignancy rate ranged from 0% to 0.8% (3844). Most complicated cysts and clustered microcysts with a palpable or mammographic correlate are classified as BI-RADS 3 and require short-interval imaging follow-up or, occasionally, US-guided aspiration. However, in the screening US setting, if multiple and bilateral complicated and simple cysts are present (ie, at least three cysts with at least one cyst in each breast), these complicated cysts can be assessed as benign, BI-RADS 2, requiring no additional follow-up (38).

Complicated cysts should never demonstrate internal vascularity at color Doppler interrogation. The presence of a solid component, mural nodule, thickened septation, or thickened wall within a cystic mass precludes the diagnosis of a benign complicated cyst. These complex masses require biopsy, as some cancers may have cystic components. The application of compound imaging and harmonics, color Doppler, and potentially elastography may help differentiate benign complicated cysts from malignant cystic-appearing masses and reduce the need for additional follow-up or biopsy.

Solid Masses

Sonographic features of benign-appearing solid masses include an oval or ellipsoid shape, “wider-than-tall” orientation parallel to the skin, circumscribed margins, gentle and smooth (less than three) lobulations, as well as absence of any malignant features (2,45) (Fig 2b). Lesions with these features are commonly fibroadenomas or other benign masses and can often be safely followed, even if the mass is palpable (4648). Malignant features of solid masses include spiculations, angular margins, marked hypoechogenicity, posterior acoustic shadowing, microcalcifications, ductal extension, branching pattern, and 1–2-mm microlobulations (2,45) (Figs 1b,56). These are also often taller-than-wide lesions with a nonparallel orientation to the skin and may occasionally be associated with thickened Cooper ligaments and/or or skin thickening. Most cancers have more than one malignant feature, spiculation being the most specific and angular margins the most common (2).

There is, however, considerable overlap between these benign and malignant US features and careful scanning technique, as well as direct correlation with mammography, is essential. For example, some high-grade invasive ductal carcinomas with central necrosis, as well as the well-differentiated mucinous and medullary subtypes, may present as circumscribed, oval, hypoechoic masses that may look like complicated cysts with low-level internal echoes at US. Benign focal fibrous breast tissue or postoperative scars can appear as irregular shadowing masses on US images. Furthermore, while echogenic lesions are often benign and frequently represent lipomas or fibrous tissue, echogenic cancers do rarely occur (Figs 78) (49,50). The presence of a single malignant feature, despite the presence of multiple benign features, precludes a benign classification and mandates biopsy, with the exception of fat necrosis and postoperative scars exhibiting typical benign mammographic features. Likewise, a mass with a benign US appearance should be biopsied if it exhibits any suspicious mammographic features.




 Ductal Carcinoma in Situ

Ductal carcinoma in situ (DCIS) is characteristically associated with microcalcifications detected at mammography, but may also be detected at US since they are often associated with a subtle hypoechoic mass, which may indicate an invasive mammographically occult component. US features associated with DCIS most commonly include a hypoechoic mass with an irregular shape, microlobulated margins, no posterior acoustic features, and no internal vascularity. Ductal abnormalities, intracystic lesions, and architectural distortions may also be present (5153). Noncalcified DCIS manifesting as a solid mass at US is more frequently found in non–high-grade than high-grade DCIS, which is more often associated with microcalcifications and ductal changes (54). US can depict microcalcifications, particularly those in clusters greater than 10 mm in size and located in a hypoechoic mass or a ductlike structure (Fig 9) (55). Malignant calcifications are more likely to be detected sonographically than are benign calcifications, which may be obscured by surrounding echogenic breast tissue (55,56). Although US is inferior to mammography in the detection of suspicious microcalcifications, the main benefit of US detection of DCIS is to identify the invasive component and guide biopsy procedures.





Breast US in Clinical Practice

Current indications for breast US as recommended by the American College of Radiology Practice Guidelines include the evaluation of palpable abnormalities or other breast symptoms, assessment of mammographic or MR imaging–detected abnormalities, and evaluation of breast implants (57). Additionally, US is routinely used for guidance during interventional procedures, treatment planning for radiation therapy, screening in certain groups of women, and evaluation of axillary lymph nodes. Much literature has been written on these uses and a comprehensive discussion is beyond the scope of this article. A few important and timely topics, however, will be reviewed.




The BI-RADS US lexicon was introduced in 2003, and subsequently, there have been several studies assessing the accuracy of BI-RADS US classification of breast lesions. Low to moderate interobserver agreement has been found in the description of margins (especially noncircumscribed margins), echogenicity, and posterior acoustic features. Abdullah et al (58) reported low interobserver agreement especially for small masses and for malignant masses. Given the importance of margin analysis in the characterization of benign and malignant lesions, this variability is potentially problematic. Studies have also shown variable results in the use of the final assessment categories. In clinical settings, Raza et al (46) showed inconsistent use of the BI-RADS 3 (probably benign) category in 14.0% of cases when biopsy was recommended. Abdullah et al also demonstrated fair and poor interobserver agreement for BI-RADS 4 (suspicious for malignancy) a, b, and c subcategories (58). However, Henig et al (59) reported more promising results, with malignancy rates in categories 3, 4, and 5 to be similar to those seen with mammographic categorization (1.2%, 17%, and 94%, respectively).


 Evaluation of Mammographic Findings

Targeted US is complementary to diagnostic mammography because of its ability to differentiate cystic and solid lesions.US is also useful in the work up of subtle asymmetries, as it can help identify or exclude the presence of an underlying mass. True hypoechoic lesions can often be differentiated from prominent fat lobules by scanning in multiple planes, because true lesions usually do not blend or elongate into adjacent tissue. With the introduction of digital breast tomosynthesis for mammographic imaging, US will play yet another important role. As mammographic lesions can often be detected, localized, and have adequate margin assessment on 3D images, patients with lesions detected on digital breast tomosynthesis images at screening may often be referred directly to US, avoiding additional mammographic imaging and its associated costs and radiation exposure (Fig 10). This will place an even greater importance on high-quality US.






Evaluation of the Symptomatic Patient:Palpable Masses, Breast Pain, and Nipple Discharge

US is essential in the evaluation of patients with the common clinical complaint of either a palpable mass or focal persistent breast pain. Unlike focal breast pain, which may be occasionally associated with benign or malignant lesions, diffuse breast pain (bilateral or unilateral), as well as cyclic breast pain, requires only clinical follow-up, as it is usually physiologic with an extremely low likelihood of malignancy (60,61). In patients with isolated focal breast pain, the role of sonography may be limited to patient reassurance (61). In women younger than 30 years of age, with a palpable lump or focal breast pain, US is the primary imaging test, with a sensitivity and negative predictive value of nearly 100% (62). Symptomatic women older than 30 years usually require both US and mammography, and in these patients, the negative predictive value approaches 100% (63,64). Lehman et al (65) demonstrated that in symptomatic women aged 30–39 years, the risk of malignancy was 1.9% and the added value of adjunct mammography in addition to US was low. Identification of a benign-appearing solid lesion at US in a symptomatic woman can negate the need for needle biopsy, as many of these masses can safely be monitored with short-interval follow-up US (4648), usually performed at 6 months. A suspicious mass identified at US can promptly undergo biopsy with US guidance.

US can also be used as an alternative or an addition to ductography in patients who present with unilateral, spontaneous bloody, clear, or serosanguinous nipple discharge (66). Among women with worrisome nipple discharge, ductography can demonstrate an abnormality in 59%–82% of women (67,68), MR imaging may demonstrate a suspicious abnormality in 34% of women (68), and US has been shown to demonstrate a subareolar mass or an intraductal mass or filling defect in up to 14% of women (67). If US can be used to identify a retroareolar mass or an intraductal mass, US-guided biopsy can be performed and ductography may be avoided (Fig 11). US may be limited, however, as small peripherally located intraductal masses or masses without an associated dilated duct may not be identified. Therefore, galactography, MR imaging, and/or major duct excision may still be necessary in the symptomatic patient with a negative US examination.


Finally, in the pregnant or lactating patient who presents with a palpable breast mass, focal breast pain, or bloody nipple discharge, US is also the initial imaging modality of choice. Targeted US examination in these patients can be used to identify most benign and malignant masses, including fibroadenomas, galactocoeles, lactating adenomas, abscesses, and invasive carcinomas. In a recent study by Robbins et al (69), a negative predictive value of 100% was found among 122 lesions evaluated with US in lactating, pregnant, or postpartum women. This is much higher than the pregnancy-associated breast cancer sensitivity of mammography, which has been reported in the range of 78%–87% (70,71). The diminished sensitivity of mammography is likely due to increased parenchymal density seen in these patients. However, since lactating breast parenchyma is more echogenic than most breast masses, hypoechoic breast cancers are more readily detected at US in pregnant patients.



Supplemental Screening Breast US

Because of the known limitations of mammography, particularly in women with dense breast tissue, supplemental screening with whole-breast US, in addition to mammography, is increasingly gaining widespread acceptance. Numerous independent studies have demonstrated that the addition of a single screening or whole-breast US examination in women with dense breast tissue at mammography will yield an additional 2.3–4.6 mammographically occult cancers per 1000 women (7280). Mammographically occult cancers detected on US images are generally small node-negative invasive cancers (Fig 12) (81). However, few studies have investigated the performance of incident screening breast US, and the optimal screening US interval is unknown. Berg and colleagues (82) recently demonstrated that incident annual supplemental screening US in intermediate- and high-risk women with mammographically dense breast tissue enabled detection of an additional 3.7 cancers per 1000 women screened.


Handheld screening breast US is highly operator-dependent and the majority of screening breast US studies have relied on physician-performed examinations. As per the ACRIN 6666 protocol, a normal screening US examination should consist of a minimum of one image in each quadrant and one behind the nipple (83). Two studies have also demonstrated that technologist-performed handheld screening breast US can achieve similar cancer detection rates (76,78).

Automated whole-breast US is a recently developed alternative to traditional handheld screening breast US, in which standardized, uniform image sets may be readily obtained by a nonradiologist. Automated whole-breast US systems may utilize a standard US unit and a linear-array transducer attached to a computer-guided mechanical arm or a dedicated screening US unit with a 15-cm wide transducer (84,85). With these systems, over 3000 overlapping sagittal, transverse, and coronal images are obtained and available for later review by the radiologist, with associated 3D reconstruction. The advantages include less operator dependence, increased radiologist efficiency, and increased reproducibility, which could aid in follow-up of lesions.

A multi-institutional study has shown that supplemental automated whole-breast US can depict an additional 3.6 cancer per 1000 women screened, similar to physician-performed handheld screening US (85). However, disadvantages include the limited ability to scan the entire breast, particularly posterior regions in large breasts, time-consuming review of a large number of images by the radiologist, and the need to recall patients for a second US examination to re-evaluate indeterminate findings. Moreover, few investigators have compared the use of handheld with automated breast US screening. A single small recent study by Chang et al (86) demonstrated that of 14 cancers initially detected at handheld screening, only 57%–79% were also detected by three separate readers on automated whole-breast US images, with the two cancers missed by all three readers at automated whole-breast US, each less than 1 cm in size.

The use of supplemental screening breast US, performed in addition to mammography, remains controversial despite proof of the ability to detect small mammographically occult cancers. US has limited value for the detection of small clustered microcalcifications without an associated mass lesion. Low positive predictive values of biopsies performed of less than 12% have been consistently reported (77,87). No outcome study has been able to demonstrate a direct decrease in patient mortality due to the detection of these additional small and mammographically occult cancers. This would require a long, randomized screening trial, which is not feasible. Rationally, however, the early detection and treatment of additional small breast cancers should improve outcomes and reduce overall morbidity and mortality. Many insurance companies will not reimburse for screening breast US and historically, this examination has not been widely accepted in the United States.

Nevertheless, because of both the known efficacy of supplemental screening breast US and overall increased breast cancer awareness, more patients and clinicians are requesting this examination. In fact, some states now mandate that radiologists inform women of their breast density and advise them to discuss supplemental screening with their doctors. Although supplemental screening breast MR imaging is usually preferred for women who are at very high risk for breast cancer (ie, women with a lifetime risk of over 20%, for example those women who are BRCA positive or have multiple first-degree relatives with a history of premenopausal breast cancer), screening breast US should be considered in women at very high risk for breast cancer who cannot tolerate breast MR imaging, as well as those women with dense breast tissue and intermediate risk (ie, lifetime risk of 15%–20%, for example those women whose only risk factor is a personal history of breast cancer or previous biopsy of a high-risk lesion), or even average risk. Future studies are needed to establish strategies to reduce false-positive results and continue to optimize both technologist-performed handheld screening US and automated whole-breast US in women with mammographically dense breast tissue.


 Use of US for MR Imaging–depicted Abnormalities

MR imaging of the breast is now an integral part of breast imaging, most commonly performed to screen high-risk women and to further assess the stage in patients with newly diagnosed breast cancers. While MR has a higher sensitivity than mammography for detecting breast cancer, the specificity is relatively low (88). Lesions detected on MR images are often mammographically occult, but many can be detected with targeted US (Fig 13). Besides further US characterization of an MR imaging–detected lesion, US may be used to guide intervention for lesions initially detected at MR imaging. US-guided biopsies are considerably less expensive, less time consuming, and more comfortable for the patient than MR imaging–guided biopsies.



Some suspicious lesions detected at MR imaging will represent invasive ductal or lobular cancers, but many may prove to be intraductal disease, which can be challenging to detect at US. Meticulous scanning technique is required for an MR imaging–directed US examination, with knowledge of subtle sonographic signs and close correlation with the MR imaging findings and location. Precontrast T1 images are helpful to facilitate localization of lesions in relation to fibroglandular tissue (89). Because MR imaging abnormalities tend to be vascular, increased vascularity may also assist in detection of a subtle sonographic correlate (90). Having the MR images available for simultaneous review while performing the US examination will ideally permit such associative correlation. At the authors’ facility, computer monitors displaying images from the picture archiving and communication system are available in all US rooms for this purpose.

Recent studies have shown that 46%–71% of lesions at MR imaging can be detected with focused US (9094). Enhancing masses detected on MR images are identified on focused US images in 58%–65% of cases compared with nonmass enhancement, which is identified on focused US images in only 12%–32% of cases (9092). Some studies have shown that US depiction of an MR imaging correlate was independent of size (91,93,95). However, Meissnitzer et al (92) showed that size dependence is also important: For masses 5 mm or smaller, only 50% were seen, versus 56% for masses 6–10 mm, 73% for masses 11–15 mm, and 86% for masses larger than 15 mm. Likewise, this study also demonstrated that for nonmass lesions, a US correlate was found for 13% of those measuring 6–10 mm, 25% of those 11–15 mm, and 42% of those larger than 15 mm (92). In addition, many of these studies determined that when a sonographic correlate was discovered, the probability of malignancy was increased (9092). Since typical US malignant features such as spiculation and posterior shadowing may be absent and the pretest probability is higher for MR imaging–detected lesions, a lower threshold for biopsy should be considered when performing MR imaging–directed US compared with routine targeted US (90) or screening US.

Because lesions are often very subtle at MR-directed US examination and because of differences in patient positioning during the two examinations, careful imaging–histologic correlation is required when performing US-guided biopsy of MR imaging–detected abnormalities. For lesions sampled with a vacuum-assisted device and US guidance, Sakamoto et al (96) found a higher rate of false-negative biopsy results for MR imaging–detected lesions than for US-detected lesions, suggesting that precise US-MR imaging correlation may not have occurred. Meissnitzer et al (92) showed that although 91% of MR imaging–detected lesions had an accurate US correlate, 9% were found to be inaccurate. With ever-improving techniques and experience in breast US, the US visualization of MR imaging–detected abnormalities will likely continue to improve. Nevertheless, if a suspicious lesion is not identified sonographically, MR imaging–guided biopsy should still be performed, because the malignancy rate of sonographically occult MR imaging–detected lesions has been shown to range from 14% to 22% (91,95).



Preoperative Staging of Cancer with US

Breast MR imaging has been shown to be more sensitive than US in the detection of additional foci of mammographically occult disease in women with newly diagnosed breast cancer (9799). Nevertheless, when a highly suspicious mass is identified at mammography and US, immediate US evaluation of the remainder of the ipsilateral breast, the contralateral breast, and the axilla should be considered. If additional lesions are identified, preoperative staging with MR imaging can be avoided and US-guided biopsy can be promptly performed, saving the patient valuable time and expense (100). In a study by Moon et al (101), of 201 patients with newly diagnosed breast cancer, staging US demonstrated mammographically occult multifocal or multicentric disease in 28 patients (14%) and contralateral breast cancers in eight patients (4%), resulting in a change in therapy in 32 patients (16%).

US can also be used to identify abnormal axillary, supraclavicular, and internal mammary lymph nodes. Abnormal lymph nodes characteristically demonstrate focal or diffuse cortical thickening (≥3 mm in thickness), a round (rather than oval or reniform) shape, loss of the echogenic fatty hilum and/or nonhilar, disorganized, irregular blood vessels (102,103) (Fig 14). A positive US-guided CNB or fine-needle aspiration of a clinically abnormal axillary lymph node in a patient with a known breast cancer can aid patient management, by avoiding the need for sentinel node biopsy and allowing the patient instead to proceed directly to axillary lymph node dissection or neoadjuvant chemotherapy.



 Interventional Breast US

US-guided interventional procedures have increased in volume in recent years and US is now the primary biopsy guidance technique used in many breast imaging centers. Most palpable lesions, as well as lesions detected at mammography, MR imaging, or screening US, can be sampled with US. With current high-resolution transducers, even suspicious intraductal microcalcifications may be detected and sampled.

While US-guided procedures require technical skills that must be developed and can be challenging, once mastered this technique allows precise real-time sampling of the lesion, which is not possible with either stereotactic or MR imaging–guided procedures. US-guided procedures do not require ionizing radiation or intravenous contrast material. US procedures are more tolerable for patients than stereotactic (104) or MR imaging–guided procedures because US-guided procedures are faster and more comfortable, as breast compression and uncomfortable biopsy coils or tables are not necessary and the procedure may be performed with the patient supine (104106).

Most literature has shown that automated 14-gauge CNB devices are adequate for the majority of US-guided biopsies (107115). Image-guided CNB is preferable to fine-needle aspiration cytology of breast masses because of superior sensitivity, specificity, and diagnostic accuracy (116). DCIS, malignant invasion, and hormone receptor status of invasive breast cancers can be determined with CNB samples, but not with fine-needle aspiration cytology. Fine-needle aspiration may be performed, however, in complicated cysts and symptomatic simple cysts. In these cases, the cyst aspirate fluid can often be discarded; cytology is usually only necessary if the fluid is frankly bloody (117).

The choice of performing fine-needle aspiration or CNB of a suspicious axillary lymph node depends on radiologist preference and the availability of an experienced cytopathologist, although CNB is usually more accurate than fine-needle aspiration biopsy (118,119). Fine-needle aspiration may be preferred for suspicious deep lymph nodes in proximity to the axillary vessels, whereas CNB may be preferred in large nodes with thickened cortices, particularly if determination of hormone receptor status or immunohistochemistry is desired, since more tissue is required for these assays. If lymphoma is suspected, a core should be placed in saline and also in conventional formalin.

While the underestimation rate of malignancy can be considerable for high-risk lesions such as atypical hyperplasia, such histology is not commonly found in lesions undergoing US-guided CNB. Multiple studies have shown a false-negative rate for US CNB biopsy of around 2%–3% (107115). Although the contiguous and larger samples obtained with a vacuum-assisted biopsy device undoubtedly reduce sampling error, the vacuum-assisted biopsy is a more expensive and more invasive procedure (109). In the authors’ experience, vacuum-assisted US biopsy is to be considered for small masses, intraductal or intracystic lesions, or lesions with subtle microcalcifications. These may be difficult to adequately sample with a spring-loaded automatic firing device. Alternatively, for more accurate sampling of such challenging cases, as well as some axillary lymph nodes and masses smaller than 1 cm in size, automated CNB needles designed to place the inner trough of the needle within a lesion before firing can be utilized (Fig 15). With this technique, the sampling trough of the CNB needle can be clearly visualized within the lesion before the overlying outer sheath is fired. Regardless of needle choice, a postbiopsy clip marker should be placed followed by a postbiopsy mammogram to document clip position. This will assist with follow-up imaging, facilitating mammography and/or MR imaging correlation.




There has been recent interest in the percutaneous removal of benign breast lesions by using US-guided vacuum-assisted biopsy. While in general, proved benign concordant lesions can safely remain in the breast, some patients desire removal. Percutaneous US-guided removal with a vacuum-assisted biopsy device can replace surgical removal in some cases, particularly for small lesions (1 cm in size or less). Several reports have shown promising results demonstrating rates of complete lesion excision, varying from 61% to 94% (120124). Dennis et al (125) demonstrated that vacuum-assisted US-guided biopsy could be used to excise intraductal lesions resulting in resolution of problematic nipple discharge in 97% of patients. Even on long-term follow-up, most studies show low rates of residual masses, more commonly observed in larger fibroadenomas.


 Intraoperative Breast US

The use of two-dimensional and 3D intraoperative US may decrease the incidence of positive margins and decrease re-excision rates (126130) particularly in the setting of lumpectomy for palpable cancers, when US is used to assess the adequacy of surgical margins to determine the need for additional tissue removal. Similarly, intraoperative US has also been utilized to improve detection and removal of metastatic lymph nodes during sentinel lymph node assessment (131).


Future Directions

Intravenous US microbubble contrast agents have been used to enhance US diagnosis by means of analysis, enhancement patterns, the rates of uptake and washout, and identification of tumor angiogenesis. In addition, preliminary research has shown that intravenous US contrast agents may be able to depict tissue function with the potential to deliver targeted gene therapy to selected tumor cells (132). However, there are currently no intravenous US contrast agents approved for use in breast imaging by the U.S. Food and Drug Administration. Other potential advances in breast US include fusion imaging, which involves the direct overlay of correlative MR imaging with targeted US. Another evolving area is that of US computer-aided detection, which may be of particular benefit when combined with automated whole-breast screening US.



Technical advances in US now allow comprehensive US diagnosis, management, and treatment of breast lesions. Optimal use of US technology, meticulous scanning technique with careful attention to lesion morphology, and recognition and synthesis of findings from multiple imaging modalities are essential for optimal patient management. In the future, as radiologists utilize US for an ever-increasing scope of indications, become aware of the more subtle sonographic findings of breast cancer, and apply newly developing tools, the value of breast US will likely continue to increase and evolve.



  • • Breast US is operator dependent; knowledge and understanding of the various technical options currently available are important for image optimization and accurate diagnosis.
  • • US is an interactive, dynamic modality and real-time scanning is necessary to assess subtle findings associated with malignancy.
  • • Ability to synthesize the information obtained from the breast US examination with concurrent mammography, MR imaging, and clinical breast examination is necessary for accurate diagnosis.
  • • The use of screening breast US in addition to mammography, particularly in women with dense breast tissue, is becoming more widely accepted in the United States.
  • • Breast US guidance is the primary biopsy method used in most breast imaging practices, and the radiologist should be familiar with various biopsy devices and techniques to adequately sample any breast mass identified at US.


Disclosures of Conflicts of Interest: R.J.H. No relevant conflicts of interest to disclose. L.M.S. Financial activities related to the present article: none to disclose. Financial activities not related to the present article: educational consultant in vascular US to Philips Healthcare; payment for lectures on breast US from Educational Symposia; payment for development of educational presentations from Philips Healthcare. Other relationships: none to disclose. L.E.P. Financial activities related to the present article: none to disclose. Financial activities not related to the present article: consultant to Hologic. Other relationships: none to disclose.


BI-RADS = Breast Imaging and Reporting Data System

CNB = core needle biopsy

DCIS = ductal carcinoma in situ

3D = three dimensional


    1. Dempsey PJ

    . The history of breast ultrasound. J Ultrasound Med2004;23(7):887–894.

    1. Stavros AT,
    2. Thickman D,
    3. Rapp CL,
    4. Dennis MA,
    5. Parker SH,
    6. Sisney GA

    . Solid breast nodules: use of sonography to distinguish between benign and malignant lesions. Radiology 1995;196(1):123–134.

    1. Mainiero MB,
    2. Goldkamp A,
    3. Lazarus E,
    4. et al

    . Characterization of breast masses with sonography: can biopsy of some solid masses be deferred? J Ultrasound Med 2005;24(2):161–167.

    1. Graf O,
    2. Helbich TH,
    3. Hopf G,
    4. Graf C,
    5. Sickles EA

    . Probably benign breast masses at US: is follow-up an acceptable alternative to biopsy? Radiology2007;244(1):87–93.

    1. Mendelson EB,
    2. Baum JK,
    3. Berg WA,
    4. Merritt CR,
    5. Rubin E

    . Breast Imaging Reporting Data System. BI-RADS: Ultrasound. Reston, Va: American College of Radiology, 2003.

    1. Berg WA,
    2. Blume JD,
    3. Cormack JB,
    4. Mendelson EB

    . Training the ACRIN 6666 Investigators and effects of feedback on breast ultrasound interpretive performance and agreement in BI-RADS ultrasound feature analysis. AJR Am J Roentgenol 2012;199(1):224–235.

    1. Stafford RJ,
    2. Whitman GJ

    . Ultrasound physics and technology in breast imaging. Ultrasound Clin 2011;6(3):299–312.

    1. Weinstein SP,
    2. Conant EF,
    3. Sehgal C

    . Technical advances in breast ultrasound imaging. Semin Ultrasound CT MR 2006;27(4):273–283.

    1. Athanasiou A,
    2. Tardivon A,
    3. Ollivier L,
    4. Thibault F,
    5. El Khoury C,
    6. Neuenschwander S

    . How to optimize breast ultrasound. Eur J Radiol2009;69(1):6–13.

    1. Kremkau FW

    . Sonography principles and instruments. 8th ed. St Louis, Mo: Elsevier-Saunders, 2011.

    1. Goss SA,
    2. Johnston RL,
    3. Dunn F

    . Comprehensive compilation of empirical ultrasonic properties of mammalian tissues. J Acoust Soc Am1978;64(2):423–457.

    1. Napolitano D,
    2. Chou CH,
    3. McLaughlin G,
    4. et al

    . Sound speed correction in ultrasound imaging. Ultrasonics 2006;44(Suppl 1):e43–e46.

    1. Barr RG,
    2. Rim A,
    3. Graham R,
    4. Berg W,
    5. Grajo JR

    . Speed of sound imaging: improved image quality in breast sonography. Ultrasound Q2009;25(3):141–144.

    1. Stavros AT

    . Breast ultrasound. Philadelphia, Pa: Lippincott, Williams & Wilkins, 2004.

    1. Cosgrove DO,
    2. Kedar RP,
    3. Bamber JC,
    4. et al

    . Breast diseases: color Doppler US in differential diagnosis. Radiology 1993;189(1):99–104.

    1. Sehgal CM,
    2. Arger PH,
    3. Rowling SE,
    4. Conant EF,
    5. Reynolds C,
    6. Patton JA

    .Quantitative vascularity of breast masses by Doppler imaging: regional variations and diagnostic implications. J Ultrasound Med 2000;19(7):427–440;quiz 441–442.

    1. Birdwell RL,
    2. Ikeda DM,
    3. Jeffrey SS,
    4. Jeffrey RB Jr.

    . Preliminary experience with power Doppler imaging of solid breast masses. AJR Am J Roentgenol1997;169(3):703–707.

    1. Gokalp G,
    2. Topal U,
    3. Kizilkaya E

    . Power Doppler sonography: anything to add to BI-RADS US in solid breast masses? Eur J Radiol 2009;70(1):77–85.

    1. Tozaki M,
    2. Fukuma E

    . Does power Doppler ultrasonography improve the BI-RADS category assessment and diagnostic accuracy of solid breast lesions?Acta Radiol 2011;52(7):706–710.

    1. Mehta TS,
    2. Raza S,
    3. Baum JK

    . Use of Doppler ultrasound in the evaluation of breast carcinoma. Semin Ultrasound CT MR 2000;21(4):297–307.

    1. Horvath E,
    2. Silva C,
    3. Fasce G,
    4. et al

    . Parallel artery and vein: sign of benign nature of breast masses. AJR Am J Roentgenol 2012;198(1):W76–W82.

    1. Campbell SC,
    2. Cullinan JA,
    3. Rubens DJ

    . Slow flow or no flow? Color and power Doppler US pitfalls in the abdomen and pelvis. RadioGraphics2004;24(2):497–506.

    1. Schaefer FK,
    2. Heer I,
    3. Schaefer PJ,
    4. et al

    . Breast ultrasound elastography: results of 193 breast lesions in a prospective study with histopathologic correlation. Eur J Radiol 2011;77(3):450–456.

    1. Zhao QL,
    2. Ruan LT,
    3. Zhang H,
    4. Yin YM,
    5. Duan SX

    . Diagnosis of solid breast lesions by elastography 5-point score and strain ratio method. Eur J Radiol2012;81(11):3245–3249.

    1. Stachs A,
    2. Hartmann S,
    3. Stubert J,
    4. et al

    . Differentiating between malignant and benign breast masses: factors limiting sonoelastographic strain ratio.Ultraschall Med 2013;34(2):131–136.

    1. Bercoff J,
    2. Tanter M,
    3. Fink M

    . Supersonic shear imaging: a new technique for soft tissue elasticity mapping. IEEE Trans Ultrason Ferroelectr Freq Control2004;51(4):396–409.

    1. Itoh A,
    2. Ueno E,
    3. Tohno E,
    4. et al

    . Breast disease: clinical application of US elastography for diagnosis. Radiology 2006;239(2):341–350.

    1. Berg WA,
    2. Cosgrove DO,
    3. Doré CJ,
    4. et al

    . Shear-wave elastography improves the specificity of breast US: the BE1 multinational study of 939 masses.Radiology 2012;262(2):435–449.

    1. Athanasiou A,
    2. Tardivon A,
    3. Tanter M,
    4. et al

    . Breast lesions: quantitative elastography with supersonic shear imaging—preliminary results. Radiology2010;256(1):297–303.

    1. Evans A,
    2. Whelehan P,
    3. Thomson K,
    4. et al

    . Invasive breast cancer: relationship between shear-wave elastographic findings and histologic prognostic factors.Radiology 2012;263(3):673–677.

    1. Fleury Ede F,
    2. Fleury JC,
    3. Piato S,
    4. Roveda D Jr.

    . New elastographic classification of breast lesions during and after compression. Diagn Interv Radiol 2009;15(2):96–103.

    1. Tozaki M,
    2. Fukuma E

    . Pattern classification of ShearWave™ Elastography images for differential diagnosis between benign and malignant solid breast masses. Acta Radiol 2011;52(10):1069–1075.

    1. Barr RG,
    2. Lackey AE

    . The utility of the “bull’s-eye” artifact on breast elasticity imaging in reducing breast lesion biopsy rate. Ultrasound Q2011;27(3):151–155.

    1. Cosgrove DO,
    2. Berg WA,
    3. Doré CJ,
    4. et al

    . Shear wave elastography for breast masses is highly reproducible. Eur Radiol 2012;22(5):1023–1032.

    1. Kalmantis K,
    2. Dimitrakakis C,
    3. Koumpis C,
    4. et al

    . The contribution of three-dimensional power Doppler imaging in the preoperative assessment of breast tumors: a preliminary report. Obstet Gynecol Int 2009;2009:530579.

    1. Chadha M,
    2. Young A,
    3. Geraghty C,
    4. Masino R,
    5. Harrison L

    . Image guidance using 3D-ultrasound (3D-US) for daily positioning of lumpectomy cavity for boost irradiation. Radiat Oncol 2011;6:45.

    1. Hilton SV,
    2. Leopold GR,
    3. Olson LK,
    4. Willson SA

    . Real-time breast sonography: application in 300 consecutive patients. AJR Am J Roentgenol1986;147(3):479–486.

    1. Berg WA,
    2. Sechtin AG,
    3. Marques H,
    4. Zhang Z

    . Cystic breast masses and the ACRIN 6666 experience. Radiol Clin North Am 2010;48(5):931–987.

    1. Kolb TM,
    2. Lichy J,
    3. Newhouse JH

    . Occult cancer in women with dense breasts: detection with screening US—diagnostic yield and tumor characteristics.Radiology 1998;207(1):191–199.

    1. Buchberger W,
    2. DeKoekkoek-Doll P,
    3. Springer P,
    4. Obrist P,
    5. Dünser M

    . Incidental findings on sonography of the breast: clinical significance and diagnostic workup. AJR Am J Roentgenol 1999;173(4):921–927.

    1. Berg WA,
    2. Campassi CI,
    3. Ioffe OB

    . Cystic lesions of the breast: sonographic-pathologic correlation. Radiology 2003;227(1):183–191.

    1. Chang YW,
    2. Kwon KH,
    3. Goo DE,
    4. Choi DL,
    5. Lee HK,
    6. Yang SB

    . Sonographic differentiation of benign and malignant cystic lesions of the breast. J Ultrasound Med 2007;26(1):47–53.

    1. Daly CP,
    2. Bailey JE,
    3. Klein KA,
    4. Helvie MA

    . Complicated breast cysts on sonography: is aspiration necessary to exclude malignancy? Acad Radiol2008;15(5):610–617.

    1. Venta LA,
    2. Kim JP,
    3. Pelloski CE,
    4. Morrow M

    . Management of complex breast cysts. AJR Am J Roentgenol 1999;173(5):1331–1336.

    1. Hong AS,
    2. Rosen EL,
    3. Soo MS,
    4. Baker JA

    . BI-RADS for sonography: positive and negative predictive values of sonographic features. AJR Am J Roentgenol2005;184(4):1260–1265.

    1. Raza S,
    2. Chikarmane SA,
    3. Neilsen SS,
    4. Zorn LM,
    5. Birdwell RL

    . BI-RADS 3, 4, and 5 lesions: value of US in management—follow-up and outcome. Radiology2008;248(3):773–781.

    1. Harvey JA,
    2. Nicholson BT,
    3. Lorusso AP,
    4. Cohen MA,
    5. Bovbjerg VE

    . Short-term follow-up of palpable breast lesions with benign imaging features: evaluation of 375 lesions in 320 women. AJR Am J Roentgenol 2009;193(6):1723–1730.

    1. Graf O,
    2. Helbich TH,
    3. Fuchsjaeger MH,
    4. et al

    . Follow-up of palpable circumscribed noncalcified solid breast masses at mammography and US: can biopsy be averted? Radiology 2004;233(3):850–856.

    1. Linda A,
    2. Zuiani C,
    3. Lorenzon M,
    4. et al

    . Hyperechoic lesions of the breast: not always benign. AJR Am J Roentgenol 2011;196(5):1219–1224.

    1. Soon PS,
    2. Vallentine J,
    3. Palmer A,
    4. Magarey CJ,
    5. Schwartz P,
    6. Morris DL

    .Echogenicity of breast cancer: is it of prognostic value? Breast2004;13(3):194–199.

    1. Moon WK,
    2. Myung JS,
    3. Lee YJ,
    4. Park IA,
    5. Noh DY,
    6. Im JG

    . US of ductal carcinoma in situ. RadioGraphics 2002;22(2):269–280; discussion 280–281.

    1. Yang WT,
    2. Tse GM

    . Sonographic, mammographic, and histopathologic correlation of symptomatic ductal carcinoma in situ. AJR Am J Roentgenol2004;182(1):101–110.

    1. Izumori A,
    2. Takebe K,
    3. Sato A

    . Ultrasound findings and histological features of ductal carcinoma in situ detected by ultrasound examination alone. Breast Cancer 2010;17(2):136–141.

    1. Park JS,
    2. Park YM,
    3. Kim EK,
    4. et al

    . Sonographic findings of high-grade and non-high-grade ductal carcinoma in situ of the breast. J Ultrasound Med2010;29(12):1687–1697.

    1. Moon WK,
    2. Im JG,
    3. Koh YH,
    4. Noh DY,
    5. Park IA

    . US of mammographically detected clustered microcalcifications. Radiology 2000;217(3):849–854.

    1. Soo MS,
    2. Baker JA,
    3. Rosen EL

    . Sonographic detection and sonographically guided biopsy of breast microcalcifications. AJR Am J Roentgenol2003;180(4):941–948.

  1. ACR Practice Guideline for the Performance of a Breast Ultrasound Examination. American College of Radiology. Published 2011.
    1. Abdullah N,
    2. Mesurolle B,
    3. El-Khoury M,
    4. Kao E

    . Breast imaging reporting and data system lexicon for US: interobserver agreement for assessment of breast masses. Radiology 2009;252(3):665–672.

    1. Heinig J,
    2. Witteler R,
    3. Schmitz R,
    4. Kiesel L,
    5. Steinhard J

    . Accuracy of classification of breast ultrasound findings based on criteria used for BI-RADS.Ultrasound Obstet Gynecol 2008;32(4):573–578.

    1. Mansel R

    . Management of breast pain. In: Harris JR, Lippman ME, MorrowM, Osborne CK, eds. Diseases of the breast. 4th ed. Philadelphia, Pa:Lippincott Williams & Wilkins, 2010; 52.

    1. Leung JW,
    2. Kornguth PJ,
    3. Gotway MB

    . Utility of targeted sonography in the evaluation of focal breast pain. J Ultrasound Med 2002;21(5):521–526; quiz 528–529.

    1. Loving VA,
    2. DeMartini WB,
    3. Eby PR,
    4. Gutierrez RL,
    5. Peacock S,
    6. Lehman CD

    .Targeted ultrasound in women younger than 30 years with focal breast signs or symptoms: outcomes analyses and management implications. AJR Am J Roentgenol 2010;195(6):1472–1477.

    1. Soo MS,
    2. Rosen EL,
    3. Baker JA,
    4. Vo TT,
    5. Boyd BA

    . Negative predictive value of sonography with mammography in patients with palpable breast lesions. AJR Am J Roentgenol 2001;177(5):1167–1170.

    1. Tumyan L,
    2. Hoyt AC,
    3. Bassett LW

    . Negative predictive value of sonography and mammography in patients with focal breast pain. Breast J2005;11(5):333–337.

    1. Lehman CD,
    2. Lee CI,
    3. Loving VA,
    4. Portillo MS,
    5. Peacock S,
    6. DeMartini WB

    .Accuracy and value of breast ultrasound for primary imaging evaluation of symptomatic women 30-39 years of age. AJR Am J Roentgenol2012;199(5):1169–1177.

    1. Ballesio L,
    2. Maggi C,
    3. Savelli S,
    4. et al

    . Role of breast magnetic resonance imaging (MRI) in patients with unilateral nipple discharge: preliminary study.Radiol Med (Torino) 2008;113(2):249–264.

    1. Sabel MS,
    2. Helvie MA,
    3. Breslin T,
    4. et al

    . Is duct excision still necessary for all cases of suspicious nipple discharge? Breast J 2012;18(2):157–162.

    1. Morrogh M,
    2. Morris EA,
    3. Liberman L,
    4. Borgen PI,
    5. King TA

    . The predictive value of ductography and magnetic resonance imaging in the management of nipple discharge. Ann Surg Oncol 2007;14(12):3369–3377.

    1. Robbins J,
    2. Jeffries D,
    3. Roubidoux M,
    4. Helvie M

    . Accuracy of diagnostic mammography and breast ultrasound during pregnancy and lactation. AJR Am J Roentgenol 2011;196(3):716–722.

    1. Liberman L,
    2. Giess CS,
    3. Dershaw DD,
    4. Deutch BM,
    5. Petrek JA

    . Imaging of pregnancy-associated breast cancer. Radiology 1994;191(1):245–248.

    1. Ahn BY,
    2. Kim HH,
    3. Moon WK,
    4. et al

    . Pregnancy- and lactation-associated breast cancer: mammographic and sonographic findings. J Ultrasound Med2003;22(5):491–497; quiz 498–499.

    1. Kolb TM,
    2. Lichy J,
    3. Newhouse JH

    . Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. Radiology 2002;225(1):165–175.

    1. Buchberger W,
    2. Niehoff A,
    3. Obrist P,
    4. DeKoekkoek-Doll P,
    5. Dünser M

    . Clinically and mammographically occult breast lesions: detection and classification with high-resolution sonography. Semin Ultrasound CT MR 2000;21(4):325–336.

    1. Crystal P,
    2. Strano SD,
    3. Shcharynski S,
    4. Koretz MJ

    . Using sonography to screen women with mammographically dense breasts. AJR Am J Roentgenol2003;181(1):177–182.

    1. Gordon PB,
    2. Goldenberg SL

    . Malignant breast masses detected only by ultrasound: a retrospective review. Cancer 1995;76(4):626–630.

    1. Kaplan SS

    . Clinical utility of bilateral whole-breast US in the evaluation of women with dense breast tissue. Radiology 2001;221(3):641–649.

    1. Berg WA,
    2. Blume JD,
    3. Cormack JB,
    4. et al

    . Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. JAMA 2008;299(18):2151–2163.

    1. Hooley RJ,
    2. Greenberg KL,
    3. Stackhouse RM,
    4. Geisel JL,
    5. Butler RS,
    6. Philpotts LE

    . Screening US in patients with mammographically dense breasts: initial experience with Connecticut Public Act 09-41. Radiology2012;265(1):59–69.

    1. Leconte I,
    2. Feger C,
    3. Galant C,
    4. et al

    . Mammography and subsequent whole-breast sonography of nonpalpable breast cancers: the importance of radiologic breast density. AJR Am J Roentgenol 2003;180(6):1675–1679.

    1. Corsetti V,
    2. Houssami N,
    3. Ferrari A,
    4. et al

    . Breast screening with ultrasound in women with mammography-negative dense breasts: evidence on incremental cancer detection and false positives, and associated cost. Eur J Cancer2008;44(4):539–544.

    1. Bae MS,
    2. Han W,
    3. Koo HR,
    4. et al

    . Characteristics of breast cancers detected by ultrasound screening in women with negative mammograms. Cancer Sci2011;102(10):1862–1867.

    1. Berg WA,
    2. Zhang Z,
    3. Lehrer D,
    4. et al

    . Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk. JAMA 2012;307(13):1394–1404.

  2. ACRIN 6666: Screening Breast Ultrasound in High-Risk Women. American College of Radiology Imaging Network. Published November 9, 2007.
    1. Kelly KM,
    2. Richwald GA

    . Automated whole-breast ultrasound: advancing the performance of breast cancer screening. Semin Ultrasound CT MR2011;32(4):273–280.

    1. Kelly KM,
    2. Dean J,
    3. Lee SJ,
    4. Comulada WS

    . Breast cancer detection: radiologists’ performance using mammography with and without automated whole-breast ultrasound. Eur Radiol 2010;20(11):2557–2564.

    1. Chang JM,
    2. Moon WK,
    3. Cho N,
    4. Park JS,
    5. Kim SJ

    . Breast cancers initially detected by hand-held ultrasound: detection performance of radiologists using automated breast ultrasound data. Acta Radiol 2011;52(1):8–14.

    1. Berg WA

    . Supplemental screening sonography in dense breasts. Radiol Clin North Am 2004;42(5):845–851, vi.

    1. Morrow M,
    2. Waters J,
    3. Morris E

    . MRI for breast cancer screening, diagnosis, and treatment. Lancet 2011;378(9805):1804–1811.

    1. Hashimoto BE,
    2. Morgan GN,
    3. Kramer DJ,
    4. Lee M

    . Systematic approach to difficult problems in breast sonography. Ultrasound Q 2008;24(1):31–38.

    1. Abe H,
    2. Schmidt RA,
    3. Shah RN,
    4. et al

    . MR-directed (“second-look”) ultrasound examination for breast lesions detected initially on MRI: MR and sonographic findings. AJR Am J Roentgenol 2010;194(2):370–377.

    1. Demartini WB,
    2. Eby PR,
    3. Peacock S,
    4. Lehman CD

    . Utility of targeted sonography for breast lesions that were suspicious on MRI. AJR Am J Roentgenol 2009;192(4):1128–1134.

    1. Meissnitzer M,
    2. Dershaw DD,
    3. Lee CH,
    4. Morris EA

    . Targeted ultrasound of the breast in women with abnormal MRI findings for whom biopsy has been recommended. AJR Am J Roentgenol 2009;193(4):1025–1029.

    1. Candelaria R,
    2. Fornage BD

    . Second-look US examination of MR-detected breast lesions. J Clin Ultrasound 2011;39(3):115–121.

    1. Carbognin G,
    2. Girardi V,
    3. Calciolari C,
    4. et al

    . Utility of second-look ultrasound in the management of incidental enhancing lesions detected by breast MR imaging. Radiol Med (Torino) 2010;115(8):1234–1245.

    1. LaTrenta LR,
    2. Menell JH,
    3. Morris EA,
    4. Abramson AF,
    5. Dershaw DD,
    6. Liberman L

    .Breast lesions detected with MR imaging: utility and histopathologic importance of identification with US. Radiology 2003;227(3):856–861.

    1. Sakamoto N,
    2. Tozaki M,
    3. Higa K,
    4. Abe S,
    5. Ozaki S,
    6. Fukuma E

    . False-negative ultrasound-guided vacuum-assisted biopsy of the breast: difference with US-detected and MRI-detected lesions. Breast Cancer 2010;17(2):110–117.

    1. Hlawatsch A,
    2. Teifke A,
    3. Schmidt M,
    4. Thelen M

    . Preoperative assessment of breast cancer: sonography versus MR imaging. AJR Am J Roentgenol2002;179(6):1493–1501.

    1. Zhang Y,
    2. Fukatsu H,
    3. Naganawa S,
    4. et al

    . The role of contrast-enhanced MR mammography for determining candidates for breast conservation surgery.Breast Cancer 2002;9(3):231–239.

    1. Berg WA,
    2. Gutierrez L,
    3. NessAiver MS,
    4. et al

    . Diagnostic accuracy of mammography, clinical examination, US, and MR imaging in preoperative assessment of breast cancer. Radiology 2004;233(3):830–849.

    1. Yang W

    . Staging of breast cancer with ultrasound. Semin Ultrasound CT MR 2011;32(4):331–341.

    1. Moon WK,
    2. Noh DY,
    3. Im JG

    . Multifocal, multicentric, and contralateral breast cancers: bilateral whole-breast US in the preoperative evaluation of patients.Radiology 2002;224(2):569–576.

    1. Mainiero MB,
    2. Cinelli CM,
    3. Koelliker SL,
    4. Graves TA,
    5. Chung MA

    . Axillary ultrasound and fine-needle aspiration in the preoperative evaluation of the breast cancer patient: an algorithm based on tumor size and lymph node appearance. AJR Am J Roentgenol 2010;195(5):1261–1267.

    1. Bedi DG,
    2. Krishnamurthy R,
    3. Krishnamurthy S,
    4. et al

    . Cortical morphologic features of axillary lymph nodes as a predictor of metastasis in breast cancer: in vitro sonographic study. AJR Am J Roentgenol 2008;191(3):646–652.

    1. Mainiero MB,
    2. Gareen IF,
    3. Bird CE,
    4. Smith W,
    5. Cobb C,
    6. Schepps B

    . Preferential use of sonographically guided biopsy to minimize patient discomfort and procedure time in a percutaneous image-guided breast biopsy program. J Ultrasound Med 2002;21(11):1221–1226.

    1. Philpotts LE

    . Percutaneous breast biopsy: emerging techniques and continuing controversies. Semin Roentgenol 2007;42(4):218–227.

    1. Harvey JA,
    2. Moran RE,
    3. DeAngelis GA

    . Technique and pitfalls of ultrasound-guided core-needle biopsy of the breast. Semin Ultrasound CT MR2000;21(5):362–374.

    1. Parker SH,
    2. Jobe WE,
    3. Dennis MA,
    4. et al

    . US-guided automated large-core breast biopsy. Radiology 1993;187(2):507–511.

    1. Liberman L,
    2. Drotman M,
    3. Morris EA,
    4. et al

    . Imaging-histologic discordance at percutaneous breast biopsy. Cancer 2000;89(12):2538–2546.

    1. Philpotts LE,
    2. Hooley RJ,
    3. Lee CH

    . Comparison of automated versus vacuum-assisted biopsy methods for sonographically guided core biopsy of the breast.AJR Am J Roentgenol 2003;180(2):347–351.

    1. Shah VI,
    2. Raju U,
    3. Chitale D,
    4. Deshpande V,
    5. Gregory N,
    6. Strand V

    . False-negative core needle biopsies of the breast: an analysis of clinical, radiologic, and pathologic findings in 27 concecutive cases of missed breast cancer. Cancer2003;97(8):1824–1831.

    1. Crystal P,
    2. Koretz M,
    3. Shcharynsky S,
    4. Makarov V,
    5. Strano S

    . Accuracy of sonographically guided 14-gauge core-needle biopsy: results of 715 consecutive breast biopsies with at least two-year follow-up of benign lesions.J Clin Ultrasound 2005;33(2):47–52.

    1. Dillon MF,
    2. Hill AD,
    3. Quinn CM,
    4. O’Doherty A,
    5. McDermott EW,
    6. O’Higgins N

    . The accuracy of ultrasound, stereotactic, and clinical core biopsies in the diagnosis of breast cancer, with an analysis of false-negative cases. Ann Surg 2005;242(5):701–707.

    1. Povoski SP,
    2. Jimenez RE,
    3. Wang WP

    . Ultrasound-guided diagnostic breast biopsy methodology: retrospective comparison of the 8-gauge vacuum-assisted biopsy approach versus the spring-loaded 14-gauge core biopsy approach. World J Surg Oncol 2011;9:87.

    1. Bolívar AV,
    2. Alonso-Bartolomé P,
    3. García EO,
    4. Ayensa FG

    . Ultrasound-guided core needle biopsy of non-palpable breast lesions: a prospective analysis in 204 cases. Acta Radiol 2005;46(7):690–695.

    1. Youk JH,
    2. Kim EK,
    3. Kim MJ,
    4. Kwak JY,
    5. Son EJ

    . Analysis of false-negative results after US-guided 14-gauge core needle breast biopsy. Eur Radiol2010;20(4):782–789.

    1. Garg S,
    2. Mohan H,
    3. Bal A,
    4. Attri AK,
    5. Kochhar S

    . A comparative analysis of core needle biopsy and fine-needle aspiration cytology in the evaluation of palpable and mammographically detected suspicious breast lesions. Diagn Cytopathol2007;35(11):681–689.

    1. Ciatto S,
    2. Cariaggi P,
    3. Bulgaresi P

    . The value of routine cytologic examination of breast cyst fluids. Acta Cytol 1987;31(3):301–304.

    1. Rao R,
    2. Lilley L,
    3. Andrews V,
    4. Radford L,
    5. Ulissey M

    . Axillary staging by percutaneous biopsy: sensitivity of fine-needle aspiration versus core needle biopsy. Ann Surg Oncol 2009;16(5):1170–1175.

    1. Gong JZ,
    2. Snyder MJ,
    3. Lagoo AS,
    4. et al

    . Diagnostic impact of core-needle biopsy on fine-needle aspiration of non-Hodgkin lymphoma. Diagn Cytopathol2004;31(1):23–30.

    1. Ko ES,
    2. Han H,
    3. Lee BH,
    4. Choe H

    . Sonographic changes after removing all benign breast masses with sonographically guided vacuum-assisted biopsy.Acta Radiol 2009;50(9):968–974.

    1. Slanetz PJ,
    2. Wu SP,
    3. Mendel JB

    . Percutaneous excision: a viable alternative to manage benign breast lesions. Can Assoc Radiol J 2011;62(4):265–271.

    1. Yom CK,
    2. Moon BI,
    3. Choe KJ,
    4. Choi HY,
    5. Park YL

    . Long-term results after excision of breast mass using a vacuum-assisted biopsy device. ANZ J Surg2009;79(11):794–798.

    1. Kim MJ,
    2. Park BW,
    3. Kim SI,
    4. et al

    . Long-term follow-up results for ultrasound-guided vacuum-assisted removal of benign palpable breast mass. Am J Surg2010;199(1):1–7.

    1. Wang ZL,
    2. Liu G,
    3. Li JL,
    4. et al

    . Sonographically guided percutaneous excision of clinically benign breast masses. J Clin Ultrasound 2011;39(1):1–5.

    1. Dennis MA,
    2. Parker S,
    3. Kaske TI,
    4. Stavros AT,
    5. Camp J

    . Incidental treatment of nipple discharge caused by benign intraductal papilloma through diagnostic Mammotome biopsy. AJR Am J Roentgenol 2000;174(5):1263–1268.

    1. Bouton ME,
    2. Wilhelmson KL,
    3. Komenaka IK

    . Intraoperative ultrasound can facilitate the wire guided breast procedure for mammographic abnormalities.Am Surg 2011;77(5):640–646.

    1. Fisher CS,
    2. Mushawah FA,
    3. Cyr AE,
    4. Gao F,
    5. Margenthaler JA

    . Ultrasound-guided lumpectomy for palpable breast cancers. Ann Surg Oncol2011;18(11):3198–3203.

    1. Krekel NM,
    2. Lopes Cardozo AM,
    3. Muller S,
    4. Bergers E,
    5. Meijer S,
    6. van den Tol MP

    .Optimising surgical accuracy in palpable breast cancer with intra-operative breast ultrasound: feasibility and surgeons’ learning curve. Eur J Surg Oncol2011;37(12):1044–1050.

    1. Olsha O,
    2. Shemesh D,
    3. Carmon M,
    4. et al

    . Resection margins in ultrasound-guided breast-conserving surgery. Ann Surg Oncol 2011;18(2):447–452.

    1. DeJean P,
    2. Brackstone M,
    3. Fenster A

    . An intraoperative 3D ultrasound system for tumor margin determination in breast cancer surgery. Med Phys2010;37(2):564–570.

    1. Hsu GC,
    2. Ku CH,
    3. Yu JC,
    4. Hsieh CB,
    5. Yu CP,
    6. Chao TY

    . Application of intraoperative ultrasound to nonsentinel node assessment in primary breast cancer. Clin Cancer Res 2006;12(12):3746–3753.

    1. Kiessling F,
    2. Fokong S,
    3. Koczera P,
    4. Lederle W,
    5. Lammers T

    . Ultrasound microbubbles for molecular diagnosis, therapy, and theranostics. J Nucl Med2012;53(3):345–348.

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Controlling focused-treatment of Prostate cancer with MRI

Writer and reporter: Dror Nir, PhD.

In recent years there is a growing trend of treating prostate cancer in a way that will preserve, at least partially, the functionality of this organ. When patients are presenting at biopsy a low-grade localized disease, they might be offered focused treatment of the cancer lesion. One of the option is treatment by high-intensity focused ultrasound (HIFU).

The offering of such treatments created the need of controlling their outcome while the prostate is still inside the patient’s body. The most commonly used protocol is following up the patient’s PSA levels and performing “control” biopsies. The biopsies part is at best case; extremely unpleasant. It also bears some risk for complications.

Therefore, urologists are constantly seeking an imaging based protocol that will enable them to assess the treatment outcome without the need for biopsy. The publication I bring below presents the possibility of using MRI for this task. Although it is not recent, it contains many images that makes the story very clear for the reader.  The main weakness of the study is the small number of patients – only 15.

MR Imaging of Prostate after Treatment with High-Intensity Focused Ultrasound

Alexander P. S. Kirkham, FRCR, Mark Emberton, FRCS, Ivan M. Hoh, MRCS, Rowland O. Illing, MRCS, A. Alex Freeman, FRCP and Clare Allen, FRCR

From the Department of Imaging, University College London Hospitals NHS Foundation Trust, England (A.P.S.K., C.A.); Institute of Urology (M.E., I.M.H., R.O.I.) and Department of Histopathology (A.A.F.), University College London, England.

Address correspondence to A.P.S.K., Imaging Department, University College Hospital, 235 Euston Road, London, England NW1 2BU (e-mail:

Radiology March 2008; 246 (3) – 833-844.


Purpose: To prospectively evaluate magnetic resonance (MR) imaging findings after high-intensity focused ultrasound (HIFU) treatment of the prostate and to correlate them with clinical and histologic findings.

Materials and Methods: Local ethics committee approval and informed consent were obtained. Fifteen consecutive men aged 46–70 years with organ-confined prostate cancer underwent ultrasonographically guided ablation of the whole prostate. Postoperative MR images were obtained within 1 month (12 patients), at 1–3 months (five patients), and in all patients at 6 months. Prostate volume was measured on T2-weighted images, and enhancing tissue was measured on dynamic images after intravenous administration of gadopentetate dimeglumine. Prostate-specific antigen (PSA) level was measured at regular intervals, and transrectal biopsy was performed in each patient at 6 months after treatment.

Results: Initial post-HIFU images showed a central nonenhancing area, surrounded by an enhancing rim. At 6 months, the prostate was small (median volume reduction, 61%) and was of predominantly low signal intensity on T2-weighted images. The volume of prostate enhancing on the initial posttreatment image correlated well with serum PSA level nadir (Spearman r = 0.90, P < .001) and with volume at 6 months (Pearsonr = 0.80, P = .001). The three patients with the highest volume of enhancing prostate at the initial posttreatment acquisition had persistent cancer at 6-month biopsy.

Conclusion: MR imaging results of the prostate show a consistent sequence of changes after treatment with HIFU and can provide information to the operator about completeness of treatment.

There is currently little to offer men with localized prostate cancer between the two extremes of watchful waiting and radical treatment—most commonly prostatectomy or radiation therapy (1). Ablation of the gland has been proposed as an alternative that has the potential to completely treat the tumor while minimizing the sexual and urinary morbidity that still accompany established radical therapies (2). Several techniques have been used in the prostate—including microwave (3) and radiofrequency (4) ablation, cryotherapy (5), photodynamic therapy (6), and high-intensity focused ultrasound (HIFU) treatment (7).

HIFU is, in several respects, ideally suited to the prostate. In contrast to extracorporeal devices for the liver and kidney (8), with the transrectal approach, there is little movement of the target because of respiration or reflection by overlying bone. A focal distance of 3 or 4 cm allows the generation of coagulative necrosis in treatment voxels less than 0.2 mL and allows a treatment volume that conforms to the shape of the prostate (9)—a degree of precision that may be beyond that of other techniques. Even so, complete ablation is likely to affect periprostatic tissues, including the neurovascular bundles containing the cavernosal nerves (10) and the external urethral sphincter. Preservation of these structures—and the patient’s erectile and urinary function—must be balanced against full treatment of the gland.

Although impotence rates after HIFU treatment approach 50% (11), it is likely that in its current clinical implementation, the prostate is not being fully ablated: In published series, the recurrence rates for cancer range between 25% and 38% (7,11,12). To our knowledge, no groups have reported mean reductions in prostate volume of more than 50% (12,13), and several groups have found it difficult to treat the anterior gland (14).

If we are to improve outcomes, a fundamental requirement for HIFU treatment (and ablative technologies in general) is a method that provides anatomic information to the operator about areas that have been over- or undertreated. This might lead to modifications in future technique, and if obtained soon after treatment, might indicate the need for further ablation. Such a method might also help predict outcome earlier than established measures, such as prostate-specific antigen (PSA) measurement and biopsy.

Magnetic resonance (MR) imaging has great potential in this setting, and Rouviere et al (14) have described the appearance of the prostate on contrast material–enhanced MR images obtained up to 5 months after HIFU treatment. Rouviere et al found a good correlation between the theoretical treatment volume and the volume of nonenhancing prostate on a subsequent acquisition. The aim of our study was to prospectively evaluate MR imaging findings after HIFU treatment of the prostate and to correlate them with clinical and histologic findings.



Misonix (the European distributors of the Sonablate device) funded the phase-II European study and provided equipment and reimbursed the hospital for costs. The company has funded two authors (I.M.H. and R.O.I.) through educational awards. One author (M.E.) has acted as a paid consultant to Misonix and also received honoraria for training and teaching. Authors other than I.M.H., R.O.I., and M.E. had control of the information and data submitted for publication. Misonix was not involved in the analysis of data or the writing of this article.


We included the first 15 men at University College Hospital (age range, 46–70 years; mean age, 59 years) who were taking part in a registered phase-II multicenter European study of HIFU therapy for organ-confined prostate cancer (Table 1). The study was approved by the local ethics committee, and full written consent was obtained from each patient. The patients understood that HIFU is an experimental treatment whose long-term outcome is unknown and were offered full conventional treatment as an alternative. The study was limited to men with a serum PSA level 15 μg/L or less, Gleason score less than 8, prostate volume less than 40 mL, life expectancy more than 5 years, and age less than 80 years. There was no limit to the number of biopsy cores that had a positive finding or the amount of cancer in each core removed. Patients with a history of previous prostate surgery were excluded, as were men who had undergone androgen deprivation therapy in the 6 months prior to recruitment or had intragland prostatic calcification more than 1 cm in diameter.

Table 1.  Patients and Demographics

 table 1

 * Ratio of cores with a positive finding to cores obtained.

 † Image not available for analysis; volume was calculated by using US measurements.

The Sonablate 500 (Focus Surgery, Indianapolis, Ind) consists of a power generator, water cooling system (the Sonachill), a treatment probe, and a positioning system. The probe contains two curved rectangular piezoceramic transducers with a driving frequency of 4 MHz and focal lengths of 30 and 40 mm. During treatment, these may be driven at low energy to provide real-time diagnostic imaging or at high energy for therapeutic ablation (in situ intensity, 1300–2200 W/cm2). The probe is covered with a condom, under which cold (17°–18°C) degassed water is circulated to help protect the rectum from thermal injury.

Patients were prepared before the procedure with two phosphate enemas to empty the rectum. Oral bowel preparation was used in some patients. Treatment was performed with general anesthesia in the lithotomy position and was performed or closely supervised in every case by an author (M.E., 2 years of experience in HIFU treatment). After gentle dilation of the anal sphincter, the treatment probe was introduced with a covering of ultrasonographic (US) gel to couple it to the rectal mucosa and was held in position with an articulated arm attached to the operating table. A 16-F Foley urethral catheter was inserted using sterile technique, and a 10-mL balloon was inflated to allow the bladder neck and median sagittal plane to be seen accurately. It was removed before treatment began.

Treatment was planned by using US-acquired volumes consisting of stacks of both sagittal and transverse sections (voxel size, 2 × 3 × 30 mm) and was applied in rows that extended in the craniocaudal axis, interleaved to avoid interference from adjacent, recently treated areas. After each 3-second period of ablation, diagnostic transverse and sagittal images in the plane of treatment were obtained to permit tailoring of the energy delivery in the next voxel according to visible changes on the gray-scale image. This is an important difference from the device used by Rouviere’s group (14), in which power is planned before the treatment begins. We aimed to set the power for each voxel at a level that produced hyperechoic change due to cavitation (as described by Illing et al [15]), and we invariably treated the whole anterior prostate. Neurovascular bundles were not identified at treatment (the Sonablate device does not yet have color Doppler capability); rather, we aimed to avoid treating outside the capsule where they lie posterolaterally (10). The time between the first ablation and the point at which treatment was considered complete was 3.0–4.4 hours (mean, 3.6 hours). A 16-F urethral catheter was placed immediately after the treatment and was left in place for 2 weeks.

MR Imaging

For most preoperative examinations and for all post-HIFU imaging, we used an MR machine (Symphony or Avanto; Siemens, Erlangen, Germany) with 1.5-T magnet and a pelvic-phased array coil. Except where stated, a full protocol of T1- and T2-weighted turbo spin-echo (Siemens) images and a dynamic fat-saturated postcontrast volume acquisition were used for both preoperative diagnostic and planning imaging and for postoperative assessment of HIFU treatment (Table 2). The contrast material used was 20 mL of gadopentetate dimeglumine (Magnevist; Schering, Berlin, Germany) given intravenously at 3 mL/sec.

Table 2. MR Sequences Used at Prostate Imaging

table 2

We aimed to image patients less than 1 month after treatment and did so in 12 patients. The remaining three patients were imaged between 1 and 3 months after treatment. Two patients were imaged in both time periods. Every patient underwent a 6-month MR examination.

Image Analysis

All volume measurements (except where stated) were acquired by using planimetry of contiguous 3-mm sections (16). T2-weighted images were used for measurement of prostatic volume both before and after treatment. The amount of intermediate- or high-signal-intensity material (ie, higher than muscle) remaining within the prostate was also measured on the 6-month posttreatment T2-weighted image.

The volume of nonenhancing prostate tissue at the post-HIFU acquisition was measured by using the final dynamic postcontrast image. On the initial posttreatment image, we also measured the volume of extraprostatic tissue that was both of low signal intensity on the T1-weighted image and nonenhancing. The distance between this tissue and the rectal mucosa was measured at its narrowest point. The mean thickness of the enhancing rim surrounding the treatment volume was measured on transverse postcontrast T1-weighted spin-echo images and was calculated by dividing the area of the rim by its circumference.

The volume of persistently enhancing prostate tissue on the initial image was calculated by subtracting the nonenhancing volume from the total volume of prostate on the T2-weighted image. This could be calculated in 13 patients; one patient did not receive contrast material at the post-HIFU MR acquisition, and the other was imaged more than 2 months after treatment.

All measurements were performed by a first-year radiology fellow (A.P.S.K.) without knowledge of PSA and histologic results. Two other observers independently measured the three key parameters that were used for correlation calculations for each patient: (a) the volume of nonenhancing prostate on the initial image, (b) the total volume of the prostate on the initial image, and (c) the final prostate volume at 6 months. One was a consultant uroradiologist with more than 10 years of experience in the interpretation of prostate MR images (C.A.); the other was a third-year urology research fellow with an interest in prostate imaging (R.O.I.). For each parameter, the mean of the three observers’ measurements was calculated and used for further analysis.

PSA Measurement and Prostate Biopsy

Serum PSA level was measured before and at 1.5, 3, and 6 months after HIFU treatment. The nadir was defined as the lowest of the three values.

Biopsies were performed by an author (A.P.S.K., with 4 years of experience in prostate biopsy) by using a transrectal approach with US guidance and an 18-gauge needle with a 2-cm throw soon after the 6-month MR examination. The number of cores obtained depended on the amount of residual prostate and varied between two and 10 (median, eight cores).

Erectile Function and Continence

The International Index of Erectile Function was used to assess erectile function both before and 3 months after HIFU treatment in each patient (17). The most important question was, “How often were your erections hard enough for penetration [with or without phosphodiesterase type 5 inhibitors]?” A score of 2 (a few times in 4 weeks) to 5 (always) was, for the purposes of this article, considered evidence of intact erectile function.

Men were asked to complete the International Continence Society–validated continence function questionnaire at baseline and at 3 and 6 months after therapy. The question deemed to be most informative was how often the patient required the use of pads or adult diapers. Responses could include “never,” “not more than one per day,” “1–2 per day,” or “more than 3 per day.”

Statistical Analysis

To assess the variance of results between observers, we used the intraclass correlation coefficient (18) applied to measurements obtained by three observers of the calculated volume of enhancing prostate on the initial post-HIFU image and the 6-month prostate volume.

The Spearman rank test was used to assess the correlation between enhancing prostate volume and serum PSA level nadir, and the Pearson test was used to examine the correlation between initial enhancing prostate volume and final prostate volume. Only the patients who were imaged less than 1 month after treatment were included in the analysis. These tests were performed by using software (GraphPad Prism for Mac, version 3;

Because some of the covariance of volumes measured after treatment was likely to be due to their correlation with pretreatment prostate volume, we also applied a correction: The values were expressed as a proportion of the pretreatment volume, and a further correlation measurement was performed by using the Pearson test. In each case, a P value of less than .02 was considered to indicate a significant difference.



Up to 1 Month After Treatment

T2-weighted images.—Compared with that on the preoperative image, the prostate volume increased in every case (Table 1 and Table E1, Fig 1). The signal intensity from the prostate on T2-weighted images within the first month was always heterogeneous and variable. It was impossible to predict from the findings on T2-weighted images which areas of the prostate would enhance after intravenous contrast material administration. The periprostatic fat was also heterogeneous in signal intensity, which was consistent with edema (Fig 2).

Figure 1: Graph of change in prostate volume after HIFU treatment. Volume rises initially (less than 1 month after treatment) and is reduced in all cases at 6 months. Numbers = patient numbers.


Figure 2: MR images in patient 1 (a–d) and (e–h) patient 8 show low volume of enhancing prostate at initial imaging and small residual prostate at 6 months. Posttreatment serum PSA level was less than 0.05 μg/L in both cases.

Figure 2a:


Figure 2b:


Figure 2c:


Figure 2d:


Figure 2e:


Figure 2f:


Figure 2g:


Figure 2h:


T1-weighted images.—The prostate was of predominantly low signal intensity, although patchy areas of intermediate or high signal intensity, likely to represent hemorrhage, were a constant finding within the gland and in all but one of 28 seminal vesicles.

Postcontrast images.—In each patient, the postcontrast images showed a central area of nonenhancing tissue. This conformed to the treatment volume and was surrounded by an enhancing rim of mean thickness of 2–8 mm (median, 4 mm) that was continuous around the prostate in most patients (Fig 2; Table E1,).

The enhancing prostate varied in size and position. Part of the enhancing rim usually lay within the prostatic capsule and continued to the prostatic apex where there was almost always some enhancing tissue between the nonenhancing prostate and the external urethral sphincter. In many patients, more central areas of enhancement were seen: at the apex or base, either posteriorly or anteriorly (Table E1), and were almost always in continuity with the rim.

In every patient, the nonenhancing, low signal intensity within the prostate extended outside the gland and involved the periprostatic fat and the levator ani muscle, particularly anterolaterally (Table E1, Figs 23). This varied considerably and tended to be most prominent in those who had no residual gland enhancement and had an undetectable serum PSA level after HIFU treatment (Table E1). In several patients, the nonenhancing area extended to involve the Denonvilliers fascia. (The distance between its margin and the rectal muscle is listed in Table E1.) In one patient, a proportion of the rectal wall enhanced avidly, but in no patient was there loss of rectal wall enhancement to suggest necrosis.

Figure 3: MR images obtained near the prostate apex show incomplete treatment and persisting high signal intensity in prostate. Serum PSA level nadir = 0.61 μg/L.

Figure 3a: Patient 4:


 Figure 3b: Patient 4:


Figure 3c: Patient 4:


Figure 3d: Patient 4:


At 1–3 Months

In three patients, there was a “double rim” (Fig 4) on postcontrast images obtained at 36 and 56 days after HIFU treatment. The inner component lay within the prostate and the outer at the prostatic capsule; the intervening part was of low signal intensity on both T1- and T2-weighted images.

 Figure 4: MR images of “double rim” at 56 days after HIFU treatment.

Figure 4a: Patient 3:


Figure 4b: Patient 3:


Figure 4c: Patient 3:


Six-month Appearance

T2-weighted images.—In every patient, the volume of the prostate was reduced by more than 45% (median, 61% reduction) (Table E1). On T2-weighted images, the majority of the persisting prostate was of low signal intensity, with poor definition to the capsule and with persisting heterogeneous signal intensity to the surrounding fat. However, in 12 of 15 patients, there was persisting high or intermediate signal intensity of the prostate—up to 5.34 mL in volume and most often seen posteriorly and at the apex (Table E1, Figs 3 and 5). In many patients (for example, those in Fig 2), low-signal-intensity prostate of reduced volume surrounded a capacious prostatic cavity continuous with the urethra, which is similar to the cavity seen after transurethral resection (19).

Figure 5: MR images of incomplete treatment of tumor and positive biopsy findings in three of 10 cores at 6 months (in right lateral midzone, right lateral base, and right parasagittal base samples). Serum PSA level nadir = 1.19 μg/L.

Figure 5a: Patient 13:


Figure 5b: Patient 13:


Figure 5c: Patient 13:


Figure 5d: Patient 13:


Figure 5e: Patient 13:


Postcontrast images.—Some small areas of nonenhancing tissue persisted in eight of 14 patients, but this was less than 1 mL in all but one (patient 13, in whom 4 mL of the gland volume of 18.7 mL was nonenhancing). The levator muscle showed a normal signal intensity.

Correlation Between Initial Imaging and Later Findings

In the 12 patients who underwent the initial acquisition within 1 month of HIFU treatment, the volume of enhancing tissue on the initial posttreatment image was positively correlated with the serum PSA level nadir (Fig 6) (Spearman r = 0.90, P < .001) and with the amount of residual tissue at 6 months (including all low-signal-intensity material that was likely to represent fibrosis or necrosis) (Fig 7) (Pearson r = 0.80, P = .001).

 Figure 6: Graph of relationship between the proportion of the prostate still enhancing on initial image and serum PSA level nadir. There is a significant positive correlation (Spearman r = 0.90, P < .001). * = patient 13, who was included in graph but not in analysis (imaged 56 days after HIFU treatment). Patients 14 and 15 are not included because they did not undergo contrast-enhanced acquisition within 2 months of HIFU treatment. μgl−1 = μg/L.


Figure 7: Graph of relationship between the proportion of the prostate still enhancing on initial image and final volume of prostate. There is a significant positive correlation between the variables (Pearson r = 0.80, P = .001). * = patient 13, who was included in graph but not in analysis (imaged 56 days after HIFU treatment). Patients 14 and 15 are not included because they did not undergo contrast-enhanced acquisition within 2 months of HIFU treatment.


When posttreatment volumes are expressed as a proportion of pretreatment prostate volume, the correlation between enhancing tissue volume on the initial posttreatment image and the 6-month prostate volume persists (Pearson r = 0.70, P = .001).

Interobserver Correlation

The interobserver variation was excellent for the calculated volume of prostate enhancing on the initial post-HIFU image, with an intraclass correlation coefficient of 0.92, and was good for final prostate volume (intraclass correlation coefficient = 0.73).

Clinical Findings

In five patients (patients 1, 3, 8, 11, and 13), there was imaging evidence (at MR imaging or retrograde urethrography) of a stricture in the mid- or distal prostatic urethra, which was confirmed by using flow rate studies and treated by using self-catheterization or with graded urethral dilators. None have required formal urethrotomy. Patient 14 developed a bladder neck stricture, which was treated successfully by incision.

Before treatment, no men required pads or adult diapers for incontinence. At 6 months after the treatment, four men still required not more than one pad per day. In two cases, this was for reassurance rather than actual leakage.

In the 14 patients in whom there was intact erectile function (score 2–5 for the question, “How often were your erections hard enough for penetration?”) before HIFU treatment, it was intact in nine patients after the procedure. One patient had stopped trying to achieve erections, and four could not achieve penetration.

Histologic Findings

In the three patients in whom there was no high-signal-intensity peripheral zone at 6 months and with serum PSA level less than 0.05 μg/L, there was either no prostatic tissue or only a small group of acini in one core. The remaining patients had a variable amount of residual prostate at core biopsy.

Five patients had residual tumor. In three patients, it was seen in at least two cores (Table E1), and these three patients also had the largest volume of enhancing prostate on the initial post-HIFU MR image (Figs 6 and 7) and more than 2 mL of intermediate- or high-signal-intensity gland on T2-weighted images at 6 months.

In four of five patients with residual cancer, it could not be identified on either contrast-enhanced or T2-weighted images. In one patient (Fig 4), the early dynamic images showed prominent enhancement in the anterior gland, which was consistent with residual cancer found at the distal (ie, nonrectal) end of three right-sided biopsy cores. Such enhancement was not seen in patients with no cancer found at core biopsy.



We found a consistent sequence of changes at MR imaging after HIFU treatment of the whole prostate. The proportion of enhancing tissue on the initial posttreatment MR image was predictive of gland volume at 6 months and serum PSA level nadir. A strong statistical relationship between the latter and outcome has recently been demonstrated (20).

Most patients with residual cancer had evidence of incomplete ablation early (a large volume of enhancing prostate on the initial image) and late (a large volume of high-signal-intensity residual prostate on T2-weighted images at 6 months).

In some patients it was possible to achieve an undetectable serum PSA level at 6 months and entirely low signal intensity on T2-weighted images in the region of the prostate. These patients had either no or a small amount of viable prostate in one core at biopsy.

Conversely, in spite of reductions in prostate volume of more than 45% at 6 months, the majority of patients had histologic evidence of persisting viable prostate, and in a group of patients with organ-confined disease but no limit to the volume of cancer pretreatment, one-third had evidence of residual tumor.

Persisting enhancing prostatic tissue usually occurred at the periphery (or extended toward the center of the gland from it) and was particularly common at the apex and near the rectum.

Results of one previously published study (14) of post-HIFU appearances with MR imaging show a similar sequence of acute changes, although there was no attempt to quantify prostate volume at 6 months. There is also a large body of work on the MR imaging appearances with thermotherapy (whether laser [21,22] or radiofrequency [23]) and cryotherapy (24) within the prostate and other organs. The hyperenhancing rim of tissue is a constant finding in several tissues, including the liver (25), the kidney (26), and the brain (27). In the liver and the kidney, it is thin (1 mm or less) and, in most cases, has disappeared by 2 months after ablation (28). Within the prostate, the hyperenhancing rim has been shown to occur after laser ablation of benign prostatic hyperplasia (21,22) and after HIFU treatment (14).

Histologic evidence in animal models—including rabbit and porcine liver (29)—suggests that the enhancing rim corresponds to an area of inflammation and then fibrosis, with a variable amount of residual, viable tissue. How much of the rim will be viable after ablation of the prostate in humans remains uncertain. On the one hand, after HIFU treatment, core biopsy results show “partial or complete necrosis” in the rim (14). On the other, after laser ablation of benign prostatic hyperplasia, the volume of coagulative necrosis at histologic examination correlates very well with the central nonenhancing region at MR imaging, not including the rim (22). The answer is likely to be that a variable amount of the rim contains viable tissue (depending on the organ being imaged [30], the nature of the treatment, and the interval before the acquisition), and the implication is that the only reliably necrotic area at MR imaging is that which does not enhance. We have avoided the term necrosis for the nonenhancing areas of prostate seen in our current study, but from these data it is likely that the areas of prostate without enhancement are truly necrotic.

The distribution of enhancing prostate on posttreatment MR images fits with histologic evidence that “ventral, lateral and dorsal sides of the prostate” have residual viable prostatic tissue at histologic examination after HIFU treatment (31). What all of these areas have in common is proximity to the more richly vascular prostatic capsule. Is it possible that increased vascularity here results in reduced efficacy? This is another area that has been addressed by Rouviere’s group (32), who did not find a correlation between successful ablation and prostate vascularity by using power Doppler US; they conclude, as others have (33,34), that short (3-second) high-intensity bursts of focused ultrasound are unlikely to be markedly affected by blood flow. An alternate explanation is a geometric one: Centrally lying voxels are easier to treat because they may be rendered necrotic either by direct treatment or by damage to supplying vessels in the periphery.

An implication of these results is that the best strategies for minimizing complications while ensuring destruction of the cancer are likely to involve a degree of targeting: If the tumor can be imaged with MR imaging, the patient might be treated with higher power and wider margins (including periprostatic fat, muscle, or even neurovascular bundles) at the site of the cancer and with a standard intensity to the rest of the gland. An analogous approach is the wide excision, including a unilateral neurovascular bundle, of bulky tumors at radical prostatectomy (35). Such an approach may well have benefited our patients 7 and 13.

One methodologic issue that is currently unresolved relates to the timing of MR imaging. A detailed within-patient study of MR imaging changes after HIFU treatment is needed to properly describe the longitudinal changes in the appearance of the prostate. Rouviere et al (14) found that the area of nonenhancing tissue decreases by 50% at 1 month compared with that at an immediate (<1 week) post-HIFU acquisition, which suggests that for an accurate assessment of necrosis volume, the prostate should be imaged as soon as possible after treatment. Of course, perfusion would ideally be assessed during HIFU treatment so that undertreated areas could be further ablated. There is some evidence that Doppler or contrast-enhanced US (36) could play this role, but, to our knowledge, there are no studies on the correlation of immediate findings with later clinical data, such as serum PSA level or histologic examination.

We used fast low-angle shot sequences to assess enhancement because we found that the subjective assessment (together with objective measurements of signal intensity) of the dynamic series helped us identify truly nonenhancing tissue. However, the T1-weighted spin-echo postcontrast sequence would have been adequate, and we consider, as others do (22), dynamic contrast-enhanced sequences not to be an essential part of the protocol for postablation assessment. What is certain is that unenhanced T2-weighted sequences are inadequate for assessing necrosis (14,22).

Our results differ from those of other published series of HIFU treatment in the marked reduction in gland volume and absence of zonal anatomy in many patients observed at 6 months. In contrast to the study of post-HIFU MR imaging by Rouviere et al (14) who used a different device, we did not find that “HIFU-induced abnormalities seem to disappear within 3–5 months.” Rather, in several patients, it was difficult to discern any residual prostate at all at both MR and US studies. The difference probably lies in the power used for treatment and the completeness of gland coverage. The stricture rate of six of 15 is high when compared with that in published series (7,37,38) and may be related to the power used, the degree of fibrosis occurring in the prostate, and the strategy for catheterization. The latter is considered likely to be important, and we have recently changed to using a suprapubic catheter (rather than urethral) after treatment. The rate of impotence after treatment is similar to that in published series (11), as is grade I incontinence.

Our work has implications for the conduct of HIFU. The finding that the volume of enhancing prostate on the initial posttreatment image correlates well with intermediate measures, such as serum PSA level nadir and biopsy evidence of residual cancer, suggests that MR imaging can provide the operator with feedback on the effectiveness of the intervention. This information might enable modification of the technique to treat areas that have been incompletely ablated in previous patients—in our series, those areas encompassed the apex and posterior gland and rarely anterior tissue (in contrast to other study results [14]). Conversely, we might have reduced power or treatment volume at the anterolateral aspect of the gland adjacent to the levator muscle. Such feedback has been cited as a desirable attribute for ablation technology (39) and up to now has been missing.

Our study had several limitations. Although it is likely that nonenhancing areas at MR imaging represent necrosis, we do not have direct histologic evidence. Sampling error and misregistration limit the utility of core biopsies in this context. We have shown that the MR imaging appearances soon after HIFU treatment correlate with findings at 6 months, but this is not the same as outcome. A considerably longer follow-up and a larger number of patients will be necessary to determine both the ultimate efficacy of HIFU treatment and the ability of MR imaging to help predict outcome. Last, while our findings suggest that MR imaging soon after treatment may be useful to assess areas of under- and overtreatment, this is not real-time feedback and does not allow modification of the treatment as it progresses.

In summary, MR imaging results in the first 6 months after HIFU treatment show a consistent sequence of changes, and appearances in the 1st month correlate with serum PSA level nadir and imaging findings at 6 months. Such imaging results hold promise for providing feedback to the operator about the effectiveness of treatment.



  • Treatment of prostate cancer by using ablation with high-intensity focused ultrasound (HIFU) results in a consistent series of changes within the gland during 6 months seen at contrast-enhanced MR imaging.
  • Within 1 month after treatment, a central nonenhancing area is surrounded by an enhancing rim of tissue lying variably within and outside the prostate.
  • At 6 months, the gland is markedly smaller and of partly or completely low signal intensity on T2-weighted images.
  • The amount of enhancing prostate on the initial image correlates with several findings at 6 months, including serum prostate-specific antigen level nadir and prostate volume.



  • MR imaging after HIFU treatment may provide information about completeness of tumor ablation and the need for early retreatment or close monitoring in cases of incomplete coverage.



  • Trial registration: This trial started recruiting before the trial registration requirements of the International Committee of Medical Journal Editors were formalized.

See Materials and Methods for pertinent disclosures.

Author contributions: Guarantors of integrity of entire study, A.P.S.K., I.M.H., C.A.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; manuscript final version approval, all authors; literature research, A.P.S.K., M.E., I.M.H., R.O.I., C.A.; clinical studies, A.P.S.K., R.O.I., C.A.; statistical analysis, A.P.S.K.; and manuscript editing, all authors

Abbreviations:HIFU = high-intensity focused ultrasoundPSA = prostate-specific antigen



Parker C. Active surveillance: towards a new paradigm in the management of early prostate cancer. Lancet Oncol 2004; 5: 101–106.


Steineck G, Helgesen F, Adolfsson J, et al. Quality of life after radical prostatectomy or watchful waiting. N Engl J Med 2002;347:790–796.


Huidobro C, Bolmsjo M, Larson T, et al. Evaluation of microwave thermotherapy with histopathology, magnetic resonance imaging and temperature mapping. J Urol 2004;171:672–678.


Shariat SF, Raptidis G, Masatoschi M, Bergamaschi F, Slawin KM. Pilot study of radiofrequency interstitial tumor ablation (RITA) for the treatment of radio-recurrent prostate cancer. Prostate 2005;65:260–267.


Onik G. The male lumpectomy: rationale for a cancer targeted approach for prostate cryoablation—a review. Technol Cancer Res Treat2004;3:365–370.


Moore CM, Hoh IM, Bown SG, Emberton M. Does photodynamic therapy have the necessary attributes to become a future treatment for organ-confined prostate cancer? BJU Int 2005;96:754–758.


Uchida T, Ohkusa H, Nagata Y, Hyodo T, Satoh T, Irie A. Treatment of localized prostate cancer using high-intensity focused ultrasound. BJU Int2006;97:56–61.


Kennedy JE. High-intensity focused ultrasound in the treatment of solid tumours. Nat Rev Cancer 2005;5:321–327.


Uchida T, Sanghvi NT, Gardner TA, et al. Transrectal high-intensity focused ultrasound for treatment of patients with stage T1b-2n0m0 localized prostate cancer: a preliminary report. Urology 2002;59:394–398.


Walsh PC, Donker PJ. Impotence following radical prostatectomy: insight into etiology and prevention. J Urol 1982;128:492–497.


Blana A, Walter B, Rogenhofer S, Wieland WF. High-intensity focused ultrasound for the treatment of localized prostate cancer: 5-year experience.Urology 2004;63:297–300.


Gelet A, Chapelon JY, Bouvier R, et al. Transrectal high-intensity focused ultrasound: minimally invasive therapy of localized prostate cancer. J Endourol 2000;14:519–528.


Chaussy C, Thuroff S. The status of high-intensity focused ultrasound in the treatment of localized prostate cancer and the impact of a combined resection. Curr Urol Rep 2003;4:248–252.


Rouviere O, Lyonnet D, Raudrant A, et al. MRI appearance of prostate following transrectal HIFU ablation of localized cancer. Eur Urol2001;40:265–274.


Illing RO, Leslie TA, Kennedy JE, Calleary JG, Ogden CW, Emberton M. Visually directed high-intensity focused ultrasound for organ-confined prostate cancer: a proposed standard for the conduct of therapy. BJU Int 2006;98:1187–1192.


Bakker J, Olree M, Kaatee R, de Lange EE, Beek FJ. In vitro measurement of kidney size: comparison of ultrasonography and MRI. Ultrasound Med Biol 1998;24:683–688.


Rosen RC, Cappelleri JC, Smith MD, Lipsky J, Pena BM. Development and evaluation of an abridged, 5-item version of the International Index of Erectile Function (IIEF-5) as a diagnostic tool for erectile dysfunction. Int J Impot Res 1999;11:319–326.


Fleiss JL. Reliability of measurement. In: The design and analysis of clinical experiments. New York, NY: Wiley, 1986; 1–32.

Sheu MH, Chiang H, Wang JH, Chang YH, Chang CY. Transurethral resection of the prostate-related changes in the prostate gland: correlation of MRI and histopathology. J Comput Assist Tomogr 2000;24:596–599.


Uchida T, Illing RO, Cathcart PJ, Emberton M. To what extent does the prostate-specific antigen nadir predict subsequent treatment failure after transrectal high-intensity focused ultrasound therapy for presumed localized adenocarcinoma of the prostate? BJU Int 2006;98:537–539.


Mueller-Lisse UG, Heuck AF, Schneede P, et al. Postoperative MRI in patients undergoing interstitial laser coagulation thermotherapy of benign prostatic hyperplasia. J Comput Assist Tomogr 1996;20:273–278.


Boni RA, Sulser T, Jochum W, Romanowski B, Debatin JF, Krestin GP. Laser ablation-induced changes in the prostate: findings at endorectal MR imaging with histologic correlation. Radiology 1997;202:232–236.

Abstract/FREE Full Text

Zlotta AR, Djavan B, Matos C, et al. Percutaneous transperineal radiofrequency ablation of prostate tumour: safety, feasibility and pathological effects on human prostate cancer. Br J Urol 1998;81:265–275.


Vellet AD, Saliken J, Donnelly B, et al. Prostatic cryosurgery: use of MR imaging in evaluation of success and technical modifications. Radiology1997;203:653–659.

Abstract/FREE Full Text

Guan YS, Sun L, Zhou XP, Li X, Zheng XH. Hepatocellular carcinoma treated with interventional procedures: CT and MRI follow-up. World J Gastroenterol 2004;10:3543–3548.


Merkle EM, Nour SG, Lewin JS. MR imaging follow-up after percutaneous radiofrequency ablation of renal cell carcinoma: findings in 18 patients during first 6 months. Radiology 2005;235:1065–1071.

Abstract/FREE Full Text

Kahn T, Bettag M, Ulrich F, et al. MRI-guided laser-induced interstitial thermotherapy of cerebral neoplasms. J Comput Assist Tomogr1994;18:519–532.


Dromain C, de Baere T, Elias D, et al. Hepatic tumors treated with percutaneous radiofrequency ablation: CT and MR imaging follow-up. Radiology2002;223:255–262.

Abstract/FREE Full Text

Raman SS, Lu DS, Vodopich DJ, Sayre J, Lassman C. Creation of radiofrequency lesions in a porcine model: correlation with sonography, CT, and histopathology. AJR Am J Roentgenol 2000;175:1253–1258.

Abstract/FREE Full Text

Illing RO, Kennedy JE, Wu F, et al. The safety and feasibility of extracorporeal high-intensity focused ultrasound (HIFU) for the treatment of liver and kidney tumours in a Western population. Br J Cancer 2005;93:890–895.


Van Leenders GJ, Beerlage HP, Ruijter ET, de la Rosette JJ, van de Kaa CA. Histopathological changes associated with high intensity focused ultrasound (HIFU) treatment for localised adenocarcinoma of the prostate. J Clin Pathol 2000;53:391–394.

Abstract/FREE Full Text

Rouviere O, Curiel L, Chapelon JY, et al. Can color doppler predict the uniformity of HIFU-induced prostate tissue destruction? Prostate2004;60:289–297.

Billard BE, Hynynen K, Roemer RB. Effects of physical parameters on high temperature ultrasound hyperthermia. Ultrasound Med Biol1990;16:409–420.


Chen L, ter Haar G, Hill CR, et al. Effect of blood perfusion on the ablation of liver parenchyma with high-intensity focused ultrasound. Phys Med Biol 1993;38:1661–1673.


Park EL, Dalkin B, Escobar C, Nagle RB. Site-specific positive margins at radical prostatectomy: assessing cancer-control benefits of wide excision of the neurovascular bundle on a side with cancer on biopsy. BJU Int 2003;91:219–222.


Sedelaar JP, Aarnink RG, van Leenders GJ, et al. The application of three-dimensional contrast-enhanced ultrasound to measure volume of affected tissue after HIFU treatment for localized prostate cancer. Eur Urol 2000;37:559–568.


Thuroff S, Chaussy C, Vallancien G, et al. High-intensity focused ultrasound and localized prostate cancer: efficacy results from the European multicentric study. J Endourol 2003;17:673–677.


Gelet A, Chapelon JY, Bouvier R, Rouviere O, Lyonnet D, Dubernard JM. Transrectal high intensity focused ultrasound for the treatment of localized prostate cancer: factors influencing the outcome. Eur Urol 2001;40:124–129.


Gillett MD, Gettman MT, Zincke H, Blute ML. Tissue ablation technologies for localized prostate cancer. Mayo Clin Proc 2004;79:1547–1555.


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Author-Writer: Dror Nir, PhD


 When reviewing the DETECTION OF PROSTATE CANCER section on the AUA website , The first thing that catches one’s attention is the image below; clearly showing two “guys” exploring with interest what could be a CT or MRI image…..

 fig 1

But, if you bother to read the review underneath this image regarding EARLY DETECTION OF PROSTATE CANCER: AUA GUIDELINE produced by an independent group that was commissioned by the AUA to conduct a systematic review and meta-analysis of the published literature on prostate cancer detection and screening; Panel Members: H. Ballentine Carter, Peter C. Albertsen, Michael J. Barry, Ruth Etzioni, Stephen J. Freedland, Kirsten Lynn Greene, Lars Holmberg, Philip Kantoff, Badrinath R. Konety, Mohammad Hassan Murad, David F. Penson and Anthony L. Zietman – You are bound to be left with a strong feeling that something is wrong!

The above mentioned literature review was done using rigorous approach.

“The AUA commissioned an independent group to conduct a systematic review and meta-analysis of the published literature on prostate cancer detection and screening. The protocol of the systematic review was developed a priori by the expert panel. The search strategy was developed and executed

by reference librarians and methodologists and spanned across multiple databases including Ovid Medline In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Ovid Cochrane Database of Systematic Reviews, Ovid Cochrane Central Register of Controlled Trials and Scopus. Controlled vocabulary supplemented with keywords was used to search for the relevant concepts of prostate cancer, screening and detection. The search focused on DRE, serum biomarkers (PSA, PSA Isoforms, PSA kinetics, free PSA, complexed PSA, proPSA, prostate health index, PSA velocity, PSA

doubling time), urine biomarkers (PCA3, TMPRSS2:ERG fusion), imaging (TRUS, MRI, MRS, MR-TRUS fusion), genetics (SNPs), shared-decision making and prostate biopsy. The expert panel manually identified additional references that met the same search criteria”

While reading through the document, I was looking for the findings related to the roll of imaging in prostate cancer screening; see highlighted above. The only thing I found: “With the exception of prostate-specific antigen (PSA)-based prostate cancer screening, there was minimal evidence to assess the outcomes of interest for other tests.

This must mean that: Notwithstanding hundreds of men-years and tens of millions of dollars which were invested in studies aiming to assess the contribution of imaging to prostate cancer management, no convincing evidence to include imaging in the screening progress was found by a group of top-experts in a thorough and rigorously managed literature survey! And it actually  lead the AUA to declare that “Nothing new in the last 20 years”…..

My interpretation of this: It says-it-all on the quality of the clinical studies that were conducted during these years, aiming to develop an improved prostate cancer workflow based on imaging. I hope that whoever reads this post will agree that this is a point worth considering!

For those who do not want to bother reading the whole AUA guidelines document here is a peer reviewed summary:

Early Detection of Prostate Cancer: AUA Guideline; Carter HB, Albertsen PC, Barry MJ, Etzioni R, Freedland SJ, Greene KL, Holmberg L, Kantoff P, Konety BR, Murad MH, Penson DF, Zietman AL; Journal of Urology (May 2013)”

It says:

“A systematic review was conducted and summarized evidence derived from over 300 studies that addressed the predefined outcomes of interest (prostate cancer incidence/mortality, quality of life, diagnostic accuracy and harms of testing). In addition to the quality of evidence, the panel considered values and preferences expressed in a clinical setting (patient-physician dyad) rather than having a public health perspective. Guideline statements were organized by age group in years (age<40; 40 to 54; 55 to 69; ≥70).

RESULTS: With the exception of prostate-specific antigen (PSA)-based prostate cancer screening, there was minimal evidence to assess the outcomes of interest for other tests. The quality of evidence for the benefits of screening was moderate, and evidence for harm was high for men age 55 to 69 years. For men outside this age range, evidence was lacking for benefit, but the harms of screening, including over diagnosis and over treatment, remained. Modeled data suggested that a screening interval of two years or more may be preferred to reduce the harms of screening.

CONCLUSIONS: The Panel recommended shared decision-making for men age 55 to 69 years considering PSA-based screening, a target age group for whom benefits may outweigh harms. Outside this age range, PSA-based screening as a routine could not be recommended based on the available evidence. The entire guideline is available at”

Other research papers related to the management of Prostate cancer were published on this Scientific Web site:

From AUA2013: “Histoscanning”- aided template biopsies for patients with previous negative TRUS biopsies

Imaging-biomarkers is Imaging-based tissue characterization

On the road to improve prostate biopsy

State of the art in oncologic imaging of Prostate

Imaging agent to detect Prostate cancer-now a reality

Scientists use natural agents for prostate cancer bone metastasis treatment

Today’s fundamental challenge in Prostate cancer screening


Men With Prostate Cancer More Likely to Die from Other Causes

New Prostate Cancer Screening Guidelines Face a Tough Sell, Study Suggests

New clinical results supports Imaging-guidance for targeted prostate biopsy

Prostate Cancer: Androgen-driven “Pathomechanism” in Early-onset Forms of the Disease

Prostate Cancer and Nanotecnology

Prostate Cancer Cells: Histone Deacetylase Inhibitors Induce Epithelial-to-Mesenchymal Transition

Imaging agent to detect Prostate cancer-now a reality

Scientists use natural agents for prostate cancer bone metastasis treatment


Prostate Cancers Plunged After USPSTF Guidance, Will It Happen Again?

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From AUA2013: “Histoscanning”- aided template biopsies for patients with previous negative TRUS biopsies

Reporter: Dror Nir, PhD


This year’s AUA takes place in San Diego, USA.

Wednesday, May 08, 2013 10:30 AM-12:30 PM
SDCC: Room 8
Prostate Cancer: Detection & Screening (V)
Moderated Poster
Funding: none
2209: “Histoscanning”- aided template biopsies for patients with previous negative TRUS biopsies.
Oleg Apolikhin; Andrey Sivkov; Gennady Efremov; Nikolay Keshishev; Oleg Zhukov; Andrey Koryakin

Abstract: 2209
Introduction and Objectives
One of the biggest problems in the diagnosis of prostate cancer (PCa), which distinguishes it from many other solid tumors, is the difficulty of tumor imaging by means of standard visualization techniques. A transrectal ultrasound (TRUS) biopsy is mostly performed on the basis of risen PSA and is often blind – tissue specimens are taken from standard zones. Biopsy under MRI control is technically and logistically complicated and expensive, while TRUS can`t always differentiate the suspicious areas. A TRUS-based innovative technique, “Histoscanningâ€� is used in our centre for PCa identification and targeted biopsy.

Prior to template biopsy we have performed Histoscanning to 31 patients, with previous one to six negative TRUS biopsies and persistent clinical suspicion of PCa (elevated PSA, high-grade prostatic intraepithelial neoplasia (HPIN) in 4 cores or suspicious TRUS findings). Age range was 51 – 75, with PSA values 3,8 – 14,3 ng/ml. Prostate size range 22-67cc. Most of the patients (n-26) from this group received therapy with 5α-reductase inhibitors for 6 months or more. Depending on the gland size, 10-14 standardized cores were taken + 4 additional cores from the suspicious zones marked on Histoscanning report.

Histopathology identified PCa in 13 out of 31 patients , adenocarcinomas with Gleason score ranging 6-8. In 11 patients with no signs of PCa we found HPIN or low-grade PIN. Comparing histology reports with Histoscanning mapping, in 8 PCa cases we found high correlation of this method with histopathological study on the amount and location of tumor lesions and in 5 cases Histoscanning showed greater spread of lesions, with good correlation of the tumor location.

Due to the effectiveness, ease of use and the short time required for data processing, Histoscanning is a promising method for more effective targeted biopsy of the prostate.

As a result of ongoing research, we aim to evaluate sensitivity and specificity of the method, fuse it with MRI, to create a 3D model for biopsy or surgery. In the future, this data could be used for decision making on the nerve-sparing prostatectomy and minimally invasive focal treatments such as cryoablation, high-intensity focused ultrasound, radiofrequency or laser ablation.

Date & Time: May 8, 2013 10:30 AM
Session Title: Prostate Cancer: Detection & Screening (V)
Sources of Funding: none


Personal note:

On the authors’ intention to fuse HistoScanning with MRI: The authors report a very compelling clinical benefit just from using HistoScanning for guiding their biopsies. HistoScanning itself results in a 3D mapping of the prostate and the suspicious locations inside.

3D mapping of the prostate by HistoScanning analysis following motorised TRUS. the colored locations represents tissue suspicious for being cancer.

3D mapping of the prostate by HistoScanning analysis following motorised TRUS. the colored locations represents tissue suspicious for being cancer.


Fusing ultrasound & MRI images is prone to image-registration errors (e.g. due to differences in the prostate’s shape-distortion by the probe) which are larger than the accuracy sought for when performing biopsy or nerve-sparing surgery. I recommend anyone who wishes to guide biopsies and treatment based on MRI and therefore is in need for good level of localized-MRI interpretation, to rely on dedicated MRI interpretation applications and not intra-modalities image fusion.

In addition, major benefits of using HistoScanning for managing prostate cancer patients are the accessibility; A urologist can perform himself, at any time he chooses and at any place, simplicity; it only requires routine TRUS, patient-friendly; it lasts less than a minute and does not require anesthesia and low-cost; it’s ultrasound! Mixing HistoScanning with MRI will certainly eliminate these.

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