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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: Robert.Gatenby@Moffitt.org).

Abstract

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

 

Introduction

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).

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

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

 cd

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). 

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

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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).

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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.

 

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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).

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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).

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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. 

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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.

Summary

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.

 

Essentials

  • • 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.

 

Acknowledgments

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.

Footnotes

  • 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).

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Ultrasound-based Screening for Ovarian Cancer

Author: Dror Nir, PhD

Occasionally, I check for news on ovarian cancer screening. I do that for sentimental reasons; I started the HistoScanning project aiming to develop an effective ultrasound-based screening solution for this cancer.

As awareness for ovarian cancer is highest in the USA, I checked for the latest news on the NCI web-site. I found that to-date: “There is no standard or routine screening test for ovarian cancer. Screening for ovarian cancer has not been proven to decrease the death rate from the disease.

Screening for ovarian cancer is under study and there are screening clinical trials taking place in many parts of the country. Information about ongoing clinical trials is available from the NCI Web site.”

I also found that:

Estimated new cases and deaths from ovarian cancer in the United States in 2013:

  • New cases: 22,240
  • Deaths: 14,030

To get an idea on the significance of these numbers, lets compare them to the numbers related to breast cancer:

Estimated new cases and deaths from breast cancer in the United States in 2013:

  • New cases: 232,340 (female); 2,240 (male)
  • Deaths: 39,620 (female); 410 (male)

Death rate of ovarian cancer patients is almost 4 times higher than the rate in breast cancer patients!

Therefore, I decided to raise awareness to the results achieved for ovarian HistoScanning in a double-blind multicenter European study that was published in European Radiology three years ago. The gynecologists who recruited patients to this study used standard ultrasound machines of GE-Medical. I would like as well to disclose that I am one of the authors of this paper:

A new computer-aided diagnostic tool for non-invasive characterisation of malignant ovarian masses: results of a multicentre validation study, Olivier Lucidarme et.al., European Radiology, August 2010, Volume 20, Issue 8, pp 1822-1830

Abstract

Objectives

To prospectively assess an innovative computer-aided diagnostic technology that quantifies characteristic features of backscattered ultrasound and theoretically allows transvaginal sonography (TVS) to discriminate benign from malignant adnexal masses.

Methods

Women (n = 264) scheduled for surgical removal of at least one ovary in five centres were included. Preoperative three-dimensional (3D)-TVS was performed and the voxel data were analysed by the new technology. The findings at 3D-TVS, serum CA125 levels and the TVS-based diagnosis were compared with histology. Cancer was deemed present when invasive or borderline cancerous processes were observed histologically.

Results

Among 375 removed ovaries, 141 cancers (83 adenocarcinomas, 24 borderline, 16 cases of carcinomatosis, nine of metastases and nine others) and 234 non-cancerous ovaries (107 normal, 127 benign tumours) were histologically diagnosed. The new computer-aided technology correctly identified 138/141 malignant lesions and 206/234 non-malignant tissues (98% sensitivity, 88% specificity). There were no false-negative results among the 47 FIGO stage I/II ovarian lesions. Standard TVS and CA125 had sensitivities/specificities of 94%/66% and 89%/75%, respectively. Combining standard TVS and the new technology in parallel significantly improved TVS specificity from 66% to 92% (p < 0.0001).

table 3

table 4

An example of an ovary considered to be normal with TVS.

An example of an ovary considered to be normal with TVS.

The same TVS false-negative ovary with OVHS-detected foci of malignancy. The presence of an adenocarcinoma was confirmed histologically.

The same TVS
false-negative ovary with OVHS-detected foci of malignancy. The presence of an
adenocarcinoma was confirmed histologically.

Conclusions

Computer-aided quantification of backscattered ultrasound is  highly sensitive for the diagnosis of malignant ovarian masses.

 Personal note:

Based on this study a promising offer for ultrasound-based screening method for ovarian cancer was published in:  Int J Gynecol Cancer. 2011 Jan;21(1):35-43. doi: 10.1097/IGC.0b013e3182000528.: Mathematical models to discriminate between benign and malignant adnexal masses: potential diagnostic improvement using ovarian HistoScanning. Vaes EManchanda RNir RNir DBleiberg HAutier PMenon URobert A.

Regrettably, the results of these studies were never transformed into routine clinical products due to financial reasons.

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

Beta-Blockers help in better survival in ovarian cancer

Ovarian Cancer and fluorescence-guided surgery: A report

Role of Primary Cilia in Ovarian Cancer

Squeezing Ovarian Cancer Cells to Predict Metastatic Potential: Cell Stiffness as Possible Biomarker

BRCA1 a tumour suppressor in breast and ovarian cancer – functions in transcription, ubiquitination and DNA repair

Warning signs may lead to better early detection of ovarian cancer

 

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2013 – YEAR OF THE ULTRASOUND

Author – Writer: Dror Nir, PhD

To those of you who did not know, 2013 is the year of the ultrasound: http://www.ultrasound2013.org/. This initiative was launched by AIUM and its objectives:

  • Raise awareness of the value and benefits of ultrasound among patients, health care providers, and insurers
  • Provide ultrasound education and evidence-based guidelines for health care providers
  • Educate insurers about the cost savings and patient benefits associated with performing an ultrasound study when scientific evidence supports its potential effectiveness compared to other imaging modalities
  • Educate patients about the benefits of ultrasound as the appropriate imaging modality for their care
  • Encourage the incorporation of ultrasound into medical education

 Quoting from the ultrasound first web-site:

The initiative is designed to call attention to the safe, effective, and affordable advantages of ultrasound as an alternative to other imaging modalities that are more costly and/or emit radiation. For a growing number of clinical conditions, ultrasound has been shown to be equally effective in its diagnostic capability, with a distinct advantage in safety and cost over computed tomography and magnetic resonance imaging. Despite this advantage, evidence suggests that ultrasound is vastly underutilized. Ultrasound First focuses on educating health care workers, medical educators, insurers, and patients of the benefits of ultrasound in medical care. “There is growing support and public awareness for the need to reduce and carefully monitor patients’ exposure to radiation during medical imaging. The use of ultrasound as an alternative imaging modality will help achieve that goal while reducing cost,” states AIUM President Alfred Abuhamad, MD. “Many health care workers and insurers are unacquainted with the range of conditions for which ultrasound has been shown to have superior diagnostic capabilities. Disseminating this knowledge to health care workers and incorporating ultrasound in medical protocols where scientific evidence has shown its diagnostic efficacy will undoubtedly improve patient safety and reduce cost. The time to act is now.”

 A primary component of Ultrasound First is providing clinical evidence for the use of ultrasound. To that aim, the Journal of Ultrasound in Medicine has launched a special feature, the Sound Judgment Series, consisting of invited articles highlighting the clinical value of using ultrasound first in specific clinical diagnoses where ultrasound has shown comparative or superior value. Clinical conditions that will be addressed in the series include postmenopausal bleeding, right lower quadrant pain, pelvic pain, right upper quadrant pain, and shoulder pain, among others. This series will serve as an important educational resource for health care workers and educators.  On the clinical evidence page one can find reasoning for why ultrasound first. Not much related to cancer diagnosis and management. The only interesting claim is:Ultrasound-guided surgery: Its use to remove tumors from women who have palpable breast cancer is much more successful than standard surgery in excising all the cancerous tissue while sparing as much healthy tissue as possible.”

In support of this initiative The Journal of Ultrasound in Medicine has launched a special series, Sound Judgment, comprised of invited articles highlighting the clinical value of using ultrasound first in specific clinical diagnoses where ultrasound has shown comparative or superior value. So far it includes only two items related to management of cancer: Sonography of Facial Cutaneous Basal Cell Carcinoma, A First-line Imaging Technique; by Ximena Wortsman, MD, and Quantitative Assessment of Tumor Blood Flow Changes in a Murine Breast Cancer Model After Adriamycin Chemotherapy Using Contrast-Enhanced Destruction-Replenishment Sonography; by Jian-Wei Wang, MD et. al. The devoted readers of our Open Access Scientific Journal might find the article by Dr. Wortsman, MD bringing complementary information to a previous post of mine: Virtual Biopsy – is it possible?. Qouting from this article: “Cutaneous basal cell carcinoma is the most common cancer in human beings, and the face is its most frequent location. Basal cell carcinoma is rarely lethal but can generate a high degree of disfigurement. Of all imaging techniques, sonography has proven to support the diagnosis and provide detailed anatomic data on extension in all axes, the exact location, vascularity, and deeper involvement. This information can be used for improving management and the cosmetic results of patients.”

 The article gives clear presentation of the problem and includes demonstrative pictures:

f1

Figure: Basal cell carcinoma with dermal involvement (transverse view, nasal tip). Grayscale sonography (A) and 3-dimensional reconstruction (B, 5- to 8-second sweep) show a 10.1-mm (wide) × 1.4-mm (deep) well-defined hypoechoic oval lesion (between markers in A and outlined in B) that affects the dermis (d) of the left nasal wing. Notice the hyperechoic spots (arrowheads) within the lesion. The nasal cartilage (c) is unremarkable; asterisk indicates basal cell carcinoma.

Basal cell carcinoma with dermal and subcutaneous involvement (transverse view, frontal region). A, Grayscale sonography shows a 11.4-mm (wide) × 6.6-mm (deep) well-defined oval hypoechoic lesion that involves the dermis (d) and subcutaneous tissue (st). There are hyperechoic spots (arrowheads) within the tumor. B, Color Doppler sonography shows increased vascularity within the tumor (asterisk). C, Three-dimensional sonographic reconstruction (5- to 8-second sweep) highlights the lesion (asterisk, outlined); b indicates bony margin of the skull.

Basal cell carcinoma with dermal and subcutaneous involvement (transverse view, frontal region). A, Grayscale sonography shows a 11.4-mm (wide) × 6.6-mm (deep) well-defined oval hypoechoic lesion that involves the dermis (d) and subcutaneous tissue (st). There are hyperechoic spots (arrowheads) within the tumor. B, Color Doppler sonography shows increased vascularity within the tumor (asterisk). C, Three-dimensional sonographic reconstruction (5- to 8-second sweep) highlights the lesion (asterisk, outlined); b indicates bony margin of the skull.

f3

Figure: Pleomorphic presentations of basal cell carcinoma lesions on grayscale sonography (transverse views). Notice the variable shapes of the tumors.

f4

Figure: Frequently, blood flow can be detected within the tumor and its periphery, with slow-flow arteries or veins. The latter vascular data can orient the clinician about the distribution and amount of blood flow that he or she will face during surgery. Despite the fact that basal cell carcinomas usually do not present high vascularity, it should be kept in mind that many of basal cell carcinoma operations are performed in the offices of clinicians and not in the main operating rooms of large hospitals. Nevertheless, the finding of high vascularity within a clinically diagnosed basal cell carcinoma may suggest another type of skin cancer that could occasionally mimic basal cell carcinoma, such as squamous cell carcinoma, Merkel cell carcinoma, or a metastatic tumor. The above figure presents variable degrees of vascularity in basal cell carcinoma lesions going from hypovascular to hypervascular on color and power Doppler sonography (transverse views)

f5

Figure: The depth correlation between sonography (variable frequency) and histologic analysis in facial basal cell carcinoma has been reported to be excellent. Thus, the intraclass correlation coefficient for comparing thickness for the two methods (sonography and histologic analysis) that has been described in literature is 0.9 (intraclass correlation coefficient values ≥0.9 are very good; 0.70–0.89 are good; 0.50–0.69 are moderate; 0.30–049 are mediocre; and ≤0.29 are bad). Two rare sonographic artifacts have been described in basal cell carcinoma. One is the “angled border” that is produced by an inflammatory giant cell reaction underlying the tumor, which may falsely increase the apparent size of the tumor. The other is the “blurry border,” which is produced by large hypertrophy of the sebaceous glands surrounding the lesion. According to the literature, both artifacts can be recognized by a well-trained operator. The figure above presents the sonographic involvement of deeper layers such as the nasal cartilage and orbicularis muscles in the face is of critical importance and may change the decision about the type of surgery. Basal cell carcinoma with nasal cartilage involvement (3-dimensional reconstruction, 5- to 8-second sweep, transverse view, left nasal wing). Notice the extension of the tumor (asterisk, outlined) to the nasal cartilage region (c); d indicates dermis.

Basal cell carcinoma with involvement of the orbicularis muscle of the eyelid (m). Grayscale sonography (transverse view, right lower eyelid) shows that the tumor (asterisk) affects the muscle layer (arrows).

Basal cell carcinoma with involvement of the orbicularis muscle of the eyelid (m). Grayscale sonography (transverse view, right lower eyelid) shows that the tumor (asterisk) affects the muscle layer (arrows).

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Ablation Devices Market to 2023 – Global Market Forecast and Trends Analysis by Technology, Devices &amp; Applications

Reporter: Aviva Lev-Ari, PhD, RN

UPDATED on 7/31/2018

Ablation devices are at present utilized as a part of shifted medicinal services ranges, for example, gynecology, dermatology, cardiology, orthopedic, neurology and a few others. Worldwide development in inclination for negligibly intrusive methodology is driving the interest for Ablation devices. Rising awareness in patient populace about accessibility and advantages from ablation treatments and defeating the cost limitations of regular medications are additionally expected to bolster the development of this market. Where regular radiation treatments have been successful and been received because of high awareness levels, developing advances, for example, hydro-mechanical removal, microwave and aqueous are expanding trusts among patients and healthcare specialist organizations.

 

How Big is the Global Ablation Devices Market?

 

The Global Ablation Devices Market is expected to exceed more than US$ 20.99 Billion by 2023 at a CAGR of 9% in the given forecast period.

 

The major driving factors of Global Ablation Devices Market are as follows:

 

  • Increasing aging population
  • Increasing incidence of cancer and cardiovascular diseases
  • Rising adoption of minimally invasive procedures
  • Development repayment scenario in established markets
  • Expansion of next-generation ablation products and technologies
  • Growing number of ablation procedures
  • Expanding funding for the development of novel ablation device

 

The restraining factors of Global Ablation Devices Market are as follows:

 

  • Healthcare cost control measures
  • Strict regulatory approvals
  • Challenges in therapeutic procedure

SOURCE

https://www.marketresearchengine.com/ablation-devices-market

 

Ablation Devices Market to 2016 – Global Market Forecast and Trends Analysis by Technology, Devices & Applications

http://www.marketsandmarkets.com/Market-Reports/ablation-devices-market-791.html

  • Radiofrequency
  • Cryoablation
  • Microwave
  • Ultrasound
  • Hydrothermal
  • Radiation
  • Cardiac
  • Cancer
  • Gynecology

Ablation procedure refers to a minimally invasive surgical procedure which involves either destruction or removal of diseased or unnecessary tissue to cure the disease. It provides successful form of surgical option that has gradually become a popular alternative over invasive procedure amongst physicians and patients. The principal advantage of these procedures over surgery is short recovery time, short length scars, low risk of infection, less blood loss, and shorter hospital stays.

The global ablation devices market was valued at $7.5 billion in 2011 and is poised to grow at a CAGR of 10.5% to reach $12.4 billion by 2016. The ablation market is broadly segmented into two classes, namely, thermal and non-thermal technologies. Thermal segment consists of technologies such as electrical, radiation, light, radiofrequency, ultrasound, microwave, and hydrothermal and non-thermal segment includes cryoablation and hydromechanical. Ablation devices have applications in myriad clinical areas such as cancer / tumor, cardiac, ophthalmology, urology, gynecology and orthopedics.

Ablation procedures have witnessed significant growth in the recent years, which are attributable to factors such as growing healthcare expenditure, favorable demographics and cost effectiveness over tradition surgical procedures. Moreover, increasing applications in cancer and cardiac segment are fueling the market growth.

Factors such as advancements in technology, increasing demand for minimally invasive surgical procedures, growing baby boomers population (especially in U.S., Japan and Western European countries) are driving the market. The incidence cases of chronic diseases is expected to rise continuously in the coming years, because with increasing age, the risk of developing chronic diseases such as cancer, cardiovascular disorders, gynaecological, and orthopaedic problems increases. The ablation devices market for treating these diseases would show significant growth in the forecast period. The principal advantage of ablation procedures over surgery is short recovery time, short surgical timelines, low risk of infection, minimal damage to the healthy tissue, less blood loss, and shorter hospital stays.

Radiation therapy accounted for the largest share of 41% of the total ablation technologies market in 2011. The major driver of radiation therapy is the fact that it is applicable to any form of cancer ranging from soft tissue such as liver, lungs to bone metastases. Compared to most other techniques, radiation therapy is considered to be effective in all cancer scenarios, thus it is a single treatment for control of cancer used by most radiologists. It is expected that, radiation therapy devices will continue to enjoy the majority share in the ablation devices market for at least another decade owing to its broad scope of use, different methods of application, stable acceptance in population, and high level of awareness as compared to newly introduced ablation techniques such as hydrothermal, microwave and hydromechanical ablation.

Americas is the biggest market for ablation devices, followed by Europe. However, Asian countries represent the fastest growing markets and factors such as high patient pool, growing preferences to MIS, geographical expansion of market players, increased government investment in healthcare facilities especially in rural areas, westernization in life style and dietary habits, increasing healthcare expenditure & improving medical insurance plans are driving the ablation devices market.

Report includes company profiles of major players such as Accuray (U.S.), Alcon Laboratories Inc. (U.S.), AngioDynamics Inc. (U.S.), Arthrocare Corporation (U.S.), Atricure Inc. (U.S.), Biosense Webster (U.S.), Boston Scientific (U.S.), BSD Medical Corporation (U.S.), C.R. Bard Inc. (U.S.), ConMed Corporation (U.S.), Covidien (Ireland), Elekta AB ( Sweden),  Galil Medical Ltd. (Israel), Medtronic Inc. (U.S.), Misonix Inc. (U.S.), nContact Surgical Inc. (U.S.), Olympus Corporation (Japan), Smith & Nephew (U.K.), St. Jude Medical (U.S.), Urologix Inc. (U.S.) and Varian Medical Systems Inc. (U.S.).

Scope of the Report

This research report categorizes the market for ablation devices into the following segments:

Global ablation devices market, by technology

  • Thermal
    • Electrical
    • Radiation
    • Light
    • Radiofrequency
    • Ultrasound
    • Microwave
    • Hydrothermal
  • Non-thermal
    • Cryoablation
    • Hydromechanical

Global ablation devices market, by products

  • Electrical – Electrical ablators and electronic brachytherapy
  • Radiation – Brachytherapy, Intensity modulated radiation therapy, Image guided radiotherapy, Stereotactic Radiotherapy (SRT), Stereotactic body radiation therapy, Nano-radiation therapy and Proton beam therapy
  • Light – Cold lasers, Excimer lasers and ultraviolet B lasers
  • Radiofrequency – Temperature controlled devices, fluid cooled device and robotic navigation–catheter manipulation systems
  • Ultrasound – High intensity focused ultrasound, Magnetic Resonance Imaging-Guided Focused Ultrasound (MRI-FUS), Ultrasound surgical systems and shock wave therapy
  • Microwave – Microwave thermotherapy
  • Hydrothermal – Endometrial hydrothermal balloon ablation devices
  • Cryoablation – Tissue contact probe, cryogen spray probe and epidermal and subcutaneous cryoablation devices

Global ablation devices market, by applications

    • Cancer
    • Cardiac
    • Ophthalmology
    • Gynecology
    • Urology
    • Orthopedics

TABLE OF CONTENTS      


1 INTRODUCTION
1.1 KEY TAKE AWAYS
1.2 REPORT DESCRIPTION
1.3 MARKETS COVERED
1.4 STAKEHOLDERS
1.5 RESEARCH METHODOLOGY
1.5.1 MARKET SIZE
1.5.2 MARKET SHARE
1.5.3 KEY DATA POINTS FROM SECONDARY SOURCES
1.5.4 KEY DATA POINTS FROM PRIMARY SOURCES
1.5.5 ASSUMPTIONS

2 EXECUTIVE SUMMARY

3 MARKET OVERVIEW
3.1 INTRODUCTION
3.2 ABLATION TECHNOLOGIES MARKET
3.3 ABLATION APPLICATION MARKET
3.4 MARKET DYNAMICS
3.4.1 DRIVERS
3.4.1.1 Technological advancements
3.4.1.2 Increasing procedures through minimal invasive surgery
3.4.1.3 Increasing aging population with higher risk of chronic diseases
3.4.2 RESTRAINTS
3.4.2.1 Pricing and reimbursement issues
3.4.2.2 Increasing regulatory agencies pressures
3.4.3 OPPORTUNITIES & CHALLENGES
3.4.3.1 Emerging markets
3.4.3.2 Technical and educational challenges
3.5 BURNING ISSUES
3.5.1 INCREASING RESEARCH IN CARDIAC ABLATION
3.6 MARKET SHARE ANALYSIS

4 ABLATION MARKET, BY TECHNOLOGY
4.1 INTRODUCTION
4.2 THERMAL
4.2.1 ELECTRICAL
4.2.2 RADIATION
4.2.3 RADIOFREQUENCY
4.2.4 LIGHT
4.2.5 ULTRASOUND
4.2.6 MICROWAVE
4.2.7 HYDROTHERMAL
4.3 NON-THERMAL
4.3.1 CRYOTHERAPY
4.3.2 HYDROMECHANICAL

5 ABLATION TECHNOLOGY MARKET, BY PRODUCTS
5.1 ELECTRICAL
5.1.1 ELECTRICAL ABLATORS
5.1.1.1 Argon Plasma/Beam coagulators
5.1.1.2 Irreversible electroporation
5.1.2 ELECTRONIC BRACHYTHERAPY
5.2 RADIATION
5.2.1 BRACHYTHERAPY
5.2.1.1 High-Dose-Rate (HDR) brachytherapy
5.2.1.2 Pulsed-Dose-Rate brachytherapy
5.2.1.3 Permanent seed brachytherapy or Low-Dose-Rate (LDR) brachytherapy
5.2.2 STEREOTACTIC RADIOSURGERY & STEREOTACTIC RADIOTHERAPY
5.2.3 IMAGE GUIDED RADIATION THERAPY (IGRT)
5.2.4 INTENSITY-MODULATED RADIATION THERAPY (IMRT)
5.2.5 STEREOTACTIC BODY RADIATION THERAPY (SBRT)
5.2.6 PROTON BEAM THERAPY
5.3 RADIOFREQUENCY
5.3.1 TEMPERATURE CONTROLLED RADIOFREQUENCY ABLATION DEVICES
5.3.2 FLUID COOLED RF ABLATION
5.3.3 THE ROBOTIC CATHETER MANIPULATION SYSTEM
5.4 LIGHT/LASER
5.4.1 COLD LASERS
5.4.2 EXCIMER LASERS
5.5 ULTRASOUND
5.5.1 HIGH INTENSITY FOCUSED ULTRASOUND (HIFU)
5.5.2 MAGNETIC RESONANCE GUIDED ULTRASOUND MRGFUS
5.5.3 ULTRASONIC SURGICAL SYSTEMS
5.5.4 EXTRACORPOREAL SHOCKWAVE LITHOTRIPSY
5.6 MICROWAVE ABLATION
5.6.1 MICROWAVE THERMOTHERAPY
5.7 HYDROTHERMAL ABLATION
5.7.1 ENDOMETRIAL HYDROTHERMAL BALLOON ABLATION DEVICES
5.8 CRYOABLATION
5.8.1 TISSUE CONTACT PROBE
5.8.2 TISSUE SPRAY PROBE
5.8.3 EPIDERMAL AND SUBCUTANEOUS CRYOABLATION DEVICES

6 ABLATION TECHNOLOGY MARKET, BY APPLICATIONS
6.1 INTRODUCTION
6.2 CANCER
6.3 CARDIOVASCULAR
6.4 OPHTHALMOLOGY
6.5 GYNECOLOGY
6.6 UROLOGY
6.7 ORTHOPEDICS
6.8 OTHERS

7 GEOGRAPHICAL ANALYSIS
7.1 INTRODUCTION
7.2 AMERICAS
7.3 EUROPE
7.4 ASIA-PACIFIC
7.5 ROW

8 COMPETITIVE LANDSCAPE
8.1 INTRODUCTION
8.2 MERGERS & ACQUISITIONS
8.3 AGREEMENTS, PARTNERSHIPS, COLLABORATIONS, JOINT VENTURES
8.4 NEW PRODUCT LAUNCHES
8.5 PIPELINE DEVELOPMENTS
8.6 OTHER DEVELOPMENTS

9 COMPANY PROFILES
9.1 ACCURAY INC.
9.1.1 OVERVIEW
9.1.2 FINANCIALS
9.1.3 PRODUCTS & SERVICES
9.1.4 STRATEGY
9.1.5 DEVELOPMENTS
9.2 ALCON LABORATORIES INC.
9.2.1 OVERVIEW
9.2.2 FINANCIALS
9.2.3 PRODUCTS & SERVICES
9.2.4 STRATEGY
9.2.5 DEVELOPMENTS
9.3 ANGIODYNAMICS INC.
9.3.1 OVERVIEW
9.3.2 FINANCIALS
9.3.3 PRODUCTS & SERVICES
9.3.4 STRATEGY
9.3.5 DEVELOPMENTS
9.4 ARTHROCARE CORPORATION
9.4.1 OVERVIEW
9.4.2 FINANCIALS
9.4.3 PRODUCTS & SERVICES
9.4.4 STRATEGY
9.4.5 DEVELOPMENTS
9.5 ATRICURE INC.
9.5.1 OVERVIEW
9.5.2 FINANCIALS
9.5.3 PRODUCTS & SERVICES
9.5.4 STRATEGY
9.5.5 DEVELOPMENTS
9.6 BIOSENSE WEBSTER INC.
9.6.1 OVERVIEW
9.6.2 PRODUCTS & SERVICES
9.6.3 STRATEGY
9.6.4 DEVELOPMENTS
9.7 BOSTON SCIENTIFIC CORPORATION
9.7.1 OVERVIEW
9.7.2 FINANCIALS
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Last week, I came across an interesting abstract related to work that is carried-out in UCLA for several years now by Prof. Lenny Marks. Lenny participated to the development of “Artemis”. Artemis is a system that is adjunct to ultrasound and performs 3D Imaging and Navigation for Prostate Biopsy by Eigen. I thought that this deserves a complementary post to Imaging-guided biopsies: Is there a preferred strategy to choose? which I posted few weeks ago

Artemis

When men present with risk parameters for harboring prostate cancer, they are advised to undergo a transrectal ultrasound guided prostate biopsy (TRUS biopsy). Over one million biopsies are carried out in the USA ever year.

The indications for a prostate biopsy in the USA are:

·         Raised PSA above 2.5ng/ml

·         Raised age-specific PSA

·         Family history of prostate cancer

·         High PSA density > 0.15ng/ml/cc

·         High PSA velocity> 0.75 ng/ml/year or doubling time ❤ years

·         Abnormal digital rectal examination

Overall, men undergoing systematic trans-rectal ultrasound (TRUS) guided biopsy of 12 cores of prostatic tissue have approximately 1 in 4 probability of being diagnosed with prostate cancer. Of these, about half are diagnosed with low risk disease. A known problem with the current practice of TRUS biopsy, is that it is performed blind – the operator does not know where the cancer is. Therefore, many low risk cancers that do not need treating are detected and many high risk cancers are missed or incorrectly classified.

The abstract below is reporting the results of a clinical study, aimed to evaluate the potential added value in using Artemis and ultrasound-MRI image fusion when performing TRUS biopsies, as a method and system to allow urologists to progress from blind biopsies to biopsies, which are mapped, targeted and tracked.

Image fusion is the process of combining multiple images from various sources into a single representative image. Ultrasound is the imaging modality used to guide Artemis in performing the biopsies. In this study MRI is used to overcome the “blindness” regarding tumor location. More on MRI’s cancer detection reliability  can be found in my posts Imaging-guided biopsies: Is there a preferred strategy to choose? and Today’s fundamental challenge in Prostate cancer screening.

Source

Curr Opin Urol. 2013 Jan;23(1):43-50. doi: 10.1097/MOU.0b013e32835ad3ee.

MRI-ultrasound fusion for guidance of targeted prostate biopsy.

Marks LYoung SNatarajan S.  Department of Urology, David Geffen School of Medicine bCenter for Advanced Surgical and Interventional Technology, University of California, Los Angeles, Los Angeles, California, USA.

Abstract

PURPOSE OF REVIEW:

Prostate cancer (CaP) may be detected on MRI. Fusion of MRI with ultrasound allows urologists to progress from blind, systematic biopsies to biopsies, which are mapped, targeted and tracked. We herein review the current status of prostate biopsy via MRI/ultrasound fusion.

RECENT FINDINGS:

Three methods of fusing MRI for targeted biopsy have been recently described: MRI-ultrasound fusion, MRI-MRI fusion (‘in-bore’ biopsy) and cognitive fusion. Supportive data are emerging for the fusion devices, two of which received US Food and Drug Administration approval in the past 5 years: Artemis (Eigen, USA) and Urostation (Koelis, France). Working with the Artemis device in more than 600 individuals, we found that targeted biopsies are two to three times more sensitive for detection of CaP than nontargeted systematic biopsies; nearly 40% of men with Gleason score of at least 7 CaP are diagnosed only by targeted biopsy; nearly 100% of men with highly suspicious MRI lesions are diagnosed with CaP; ability to return to a prior biopsy site is highly accurate (within 1.2 ± 1.1 mm); and targeted and systematic biopsies are twice as accurate as systematic biopsies alone in predicting whole-organ disease.

SUMMARY:

In the future, MRI-ultrasound fusion for lesion targeting is likely to result in fewer and more accurate prostate biopsies than the present use of systematic biopsies with ultrasound guidance alone.

Written by: Dror Nir, PhD.

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Writer: Venkat Karra, Ph.D.

This study was reported today in the Optical Society’s (OSA) open-access journal Optics Express (Optics Express, Vol. 20, Issue 11, pp. 11582-11597 (2012)), and provide proof-of-concept support that the technology can distinguish malignant tissue by providing high-contrast images of tumors.

In breast cancer screening, x-ray mammography and ultrasonography are primarily used to understand any morphological changes of breast tissue. However, these conventional techniques have their own drawbacks because of for example ionizing radiation that could cause leukemia after prolonged/ repeated exposure where as ultrasonography is strongly operator dependent.

Tumor vascularization is a crucial feature in breast imaging. One commonly used method that focuses on tumor vascularization is Dynamic Contrast Enhanced MRI (DCE-MRI). The high sensitivity of this technique for detecting breast cancer proves that vascularity can indeed provide additional information about the nature of tissue. However, DCE-MRI suffers from a limited specificity, requires the injection of contrast agents and is relatively expensive.

Far-red and near-infrared (NIR): It is gaining attention in (non-invasively) visualizing cancer and its associated vasculature due to its ability to provide functional and molecular information without the use of ionizing radiation. In recent studies, it has been shown that optical imaging in the form of diffuse optical tomography (DOT) can indeed visualize breast malignancies, primarily because of the high absorption of hemoglobin in the NIR regime. However, DOT suffers from low spatial resolution.

Several groups have studied the feasibility of photoacoustic image (PAI) in breast imaging due to their superior resolution capabilities to that of pure optical techniques. Photoacoustic imaging exploits the high NIR light absorption contrast between benign and malignant tissue, but provides superior resolution arising from ultrasound detection.

Scientists from Center for Breast Care, Medisch Spectrum Twente hospital,  University of Twente and University of Amsterdam have developed the Twente Photoacoustic Mammoscope (PAM), to image the breast in transmission mode. The authors say that, in a first pilot study with this system in 2007, it was possible to get technically acceptable measurements on five patients with radiographically proven breast malignancies. Of those, four cases revealed a high photoacoustic contrast with respect to the background associated with tumor related vasculature. Now the authors have recently started an extended clinical study using PAM, as a continuation of the study performed in 2007.

In this new study, they have investigated the clinical feasibility of photoacoustic mammography in a larger group of patients with different types of breast lesions to obtain more information about the clinical feasibility and limitations of photoacoustic mammography and the results were compared with conventional imaging and histopathology.

Ten technically acceptable measurements on patients with malignancies (BI-RADS 5) and two measurements on patients with cysts (BI-RADS 2) were performed. In the reconstructed volumes of all ten malignant lesions, a confined region with high contrast with respect to the background was seen. In all malignant cases, the PA contrast of the abnormality was higher than the contrast on x-ray mammography. The PA contrast appeared to be independent of the mammographically estimated breast density and was absent in the case of cysts.

Authors say that technological improvements to the instrument and further studies on less suspicious lesions are planned to further investigate the potential of PAM. The authors from University of Twente hope that these early results will one day lead to the development of a safe, comfortable, and accurate alternative or adjunct to conventional techniques for detecting breast tumors.

Twente Photoacoustic Mammoscope (PAM):

This techniques combines the light-based system’s to distinguish between benign and malignant tissue with ultrasound to achieve superior targeting ability. The device is built into a hospital bed, where the patient lies prone and positions her breast for imaging. Laser light at a wavelength of 1,064 nm scans the breast. Because there is increased absorption of the light in malignant tissue the temperature slightly increases. With the rise in temperature, thermal expansion creates a pressure wave, which is detected by an ultrasound detector placed on one side of the breast. The resulting photoacoustic signals are then processed by the PAM system and reconstructed into images. These images reveal abnormal areas of high intensity (tumor tissue) as compared to areas of low intensity (benign tissue). This is one of the first times that the technique has been tested on breast cancer patients.

Note: Breast cancer is one of the most common forms of cancer among females and each year more than 450,000 women are diagnosed worldwide with the disease.

Source:

http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-20-11-11582

Reporter: Venkat Karra

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