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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 imaging as an instrument for measuring tissue elasticity: “Shear-wave Elastography” VS. “Strain-Imaging”

Writer and curator: Dror Nir, PhD

In the context of cancer-management, imaging is pivotal. For decades, ultrasound is used by clinicians to support every step in cancer pathways. Its popularity within clinicians is steadily increasing despite the perception of it being less accurate and less informative than CT and MRI. This is not only because ultrasound is easily accessible and relatively low cost, but also because advances in ultrasound technology, mainly the conversion into PC-based modalities allows better, more reproducible, imaging and more importantly; clinically-effective image interpretation.

The idea to rely on ultrasound’s physics in order to measure the stiffness of tissue lesions is not new. The motivation for such measurement has to do with the fact that many times malignant lesions are stiffer than non-malignant lesions.

The article I bring below; http://digital.studio-web.be/digitalMagazine?issue_id=254 by Dr. Georg Salomon and his colleagues, is written for lay-readers. I found it on one of the many portals that are bringing quasi-professional and usually industry-sponsored information on health issues; http://www.dieurope.com/ – The European Portal for Diagnostic Imaging. Note, that when it comes to using ultrasound as a diagnostic aid in urology, Dr. Georg Salomon is known to be one of the early adopters for new technologies and an established opinion leader who published many peer-review, frequently quoted, papers on Elastography.

The important take-away I would like to highlight for the reader: Quantified measure of tissue’s elasticity (doesn’t matter if is done by ShearWave or another “Elastography” measure implementation) is information that has real clinical value for the urologists who needs to decide on the right pathway for his patient!

Note: the highlights in the article below are added by me for the benefit of the reader.

Improvement in the visualization of prostate cancer through the use of ShearWave Elastography

by:

Dr Georg Salomon1 Dr Lars Budaeus1, Dr L Durner2 & Dr K Boe1

1. Martini-Clinic — Prostate Cancer Center University Hospital Hamburg Eppendorf Martinistrasse 52, 20253 Hamburg, Germany

2. Urologische Kilnik Dr. Castringius Munchen-Planegg Germeringer Str. 32, 82152 Planegg, Germany

Corresponding author; PD Dr. Georg Salomon

Associate Professor of Urology

Martini Clinic

Tel: 0049 40 7410 51300

gsalornon@uke.de

 

Prostate cancer is the most common cancer in males with more than 910,000 annual cases worldwide. With early detection, excellent cure rates can be achieved. Today, prostate cancer is diagnosed by a randomized transrectal ultrasound guided biopsy. However, such randomized “blind” biopsies can miss cancer because of the inability of conventional TRUS to visualize small cancerous spots in most cases.

Elastography has been shown to improve visualization of prostate cancer.

The innovative ShearWave Elastography technique is an automated, user-friendly and quantifiable method for the determination of prostatic tissue stiffness.

The detection of prostate cancer (PCA) has become easier thanks to Prostate Specific Anti­gen (PSA) testing; the diagnosis of PCA has been shifted towards an earlier stage of the disease.

Prostate cancer is, in more than 80 % of the cases, a heterogeneous and multifocal tumor. Conventional ultra­sound has limitations to accurately define tumor foci within the prostate. This is due to the fact that most PCA foci are isoechogenic, so in these cases there is no dif­ferentiation of benign and malignant tissue. Because of this, a randomized biopsy is performed under ultrasound guidance with at least 10 to 12 biopsy cores, which should represent all areas of the prostate. Tumors, however, can be missed by this biopsy regimen since it is not a lesion-targeted biopsy. When PSA is rising — which usually occurs in most men — the originally negative biopsy has to be repeated.

What urologists expect from imag­ing and biopsy procedures is the detection of prostate cancer at an early stage and an accurate description of all foci within the prostate with different (Gleason) grades of differentiation for best treatment options.

In the past 10 years a couple of new innovative ultrasound techniques (computerized, contrast enhanced and real time elastography) have been introduced to the market and their impact on the detection of early prostate cancer has been evaluated. The major benefit of elastography compared to the other techniques is its ability to provide visualization of sus­picious areas and to guide the biopsy needle, in real time, to the suspicious and potentially malignant area.

Ultrasound-based elastography has been investigated over the years and has had a lot of success for increasing the detection rate of prostate cancer or reducing the number of biopsy sam­ples required. [1-3]. Different compa­nies have used different approaches to the ultrasound elastography technique (strain elastography vs. shear wave elastography). Medical centers have seen an evolution in better image qual­ity with more stable and reproducible results from these techniques.

One drawback of real time strain elastography is that there is a sig­nificant learning curve to be climbed before reproducible elastograms can be generated. The technique has to be performed by compressing and then decompressing the ultrasound probe to derive a measurement of tissue displacement.

Today there are ultrasound scanners on the market, which have the ability to produce elastograms without this “manual” assistance: this technique is called shear-wave elastography. While the ultrasound probe is being inserted transrectally, the “elastograms” are generated automatically by the calcu­lation of shear wave velocity as the waves travel through the tissue being examined, thus providing measure­ments of tissue stiffness and not dis­placement measurements.

There are several different tech­niques for this type of elastography. The FibroScan system, which is not an ultrasound unit, uses shear waves (transient elastography) to evaluate the advancement of the stiffness of the liver. Another technique is Acous­tic Radiation Force Impulse or ARF1 technique, also used for the liver. These non-real-time techniques only provide a shear wave velocity estimation for a single region of interest and are not currently used in prostate imaging.

A shear wave technology that pro­vides specific quantification of tissue elasticity in real-time is ShearWave Elastography, developed by Super-Sonic Imagine. This technique mea­sures elasticity in kilopascals and can provide visual representation of tis­sue stiffness over the entire region of interest in a color-coded map on the ultrasound screen. On a split screen the investigator can see the conven­tional ultrasound B-mode image and the color-coded elastogram at the same time. This enables an anatomi­cal view of the prostate along with the elasticity image of the tissue to guide the biopsy needle.

In short, ShearWave Elastography (SWE) is a different elastography technique that can be used for several applications. It automatically gener­ates a real-time, reproducible, fully quantifiable color-coded image of tissue elasticity.

QUANTIFICATION OF TISSUE STIFFNESS Such quantification can help to increase the chance that a targeted biopsy is positive for cancer.

It has been shown that elastography-targeted biopsies have an up to 4.7 times higher chance to be positive for cancer than a randomized biopsy [4J. Shear-Wave Elastography can not only visual­ize the tissue stiffness in color but also quantify (in kPa) the stiffness in real time, for several organs including the prostate. Correas et al, reported that with tissue stiffness higher than 45 to 50 kPa the chance of prostate cancer is very high in patients undergoing a pros­tate biopsy. The data from Gorreas et al showed a sensitivity of 80 % and a high negative predictive value of up to 9096. Another group (Barr et A) achieved a negative predictive value of up to 99.6% with a sensitivity of 96.2% and specific­ity of 962%. With a cut-off of 4D kPa the positive biopsy rate for the ShearWave Elastography targeted biopsy was 50%, whereas for randomized biopsy it was 20.8 95. In total 53 men were enrolled in this study.

Our group used SWE prior to radical prostatectomy to determine if the Shear-Wave Elastography threshold had a high accuracy using a cutoff >55 kPa. (Fig 1)

We then compared the ShearWave results with the final histopathological results. [Figure I], Our results showed the accuracy was around 78 % for all tumor foci We were also able to verify that ShearWave Elastography targeted biopsies were more likely to be posi­tive compared to randomized biopsies. [Figures 2, 3]

F1

F2F3 

CONCLUSION

SWE is a non-invasive method to visualize prostate cancer foci with high accuracy, in a user-friendly way. As Steven Kaplan puts it in an edi­torial comment in the Journal of Urology 2013: “Obviously, large-scale studies with multicenter corroboration need to be performed. Nevertheless, SWE is a potentially promising modality to increase our efficiency in evaluating prostate diseases:’

 

REFERENCES

  1. Pallweln, L. et al-. Sonoelastography of the prostate: comparison with systematic biopsy findings in 492 patients. European journal of radiology, 2008. 65(2): p. 304-10.
  2. Pallwein, L., et al., Comparison of sono-elastography guided biopsy with systematic biopsy: Impact on prostate cancer detecton. European radiology, 2007_ 17.(9) p. 2278-85.
  3. Salomon, G., et al., Evaluation of prostate can cer detection with ultrasound real-time elas-tographyl a companion with step section path­ological analysis after radical prostatectomy. European urology, 2008. 5446): p. 135462-
  4. Aigner, F., at al., Value of real-time elastography targeted biopsy for prostate cancer detection in men with prostate specific antigen 125 ng/mi or greater and 4-00 ng/ml or Lass. The Journal of urology, 2010. 184{3): p. 813.7,

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

Imaging: seeing or imagining? (Part 1)

Early Detection of Prostate Cancer: American Urological Association (AUA) Guideline

Today’s fundamental challenge in Prostate cancer screening

State of the art in oncologic imaging of Prostate.

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

On the road to improve prostate biopsy

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EARLY DETECTION OF PROSTATE CANCER: AUA GUIDELINE

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 www.AUAnet.org/education/guidelines/prostate-cancer-detection.cfm.”

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

ROLE OF VIRAL INFECTION IN PROSTATE CANCER

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

ROLE OF VIRAL INFECTION IN PROSTATE CANCER

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

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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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Imaging-biomarkers is Imaging-based tissue characterization

Author – Writer: Dror Nir, PhD

For everyone who is skeptical about the future role of imaging-based tissue chracterisation in the management of cancer, the following “Statement paper” ESR statement on the stepwise development of imaging biomarkers published online: 9 February 2013, by the European Society of Radiology (ESR), should provide substantial reassurance that this kind of technology will become a must! In support of this claim I quote the following information:

The European Society of Radiology and its related European Institute for Biomedical Imaging Research (EIBIR) should have a relevant role in coordinating future developments of biomarkers and in the assessment and validation of imaging biomarkers as surrogate end points.

Acknowledgements

This paper was kindly prepared by the ESR Subcommittee on Imaging Biomarkers (Chairperson: Bernard Van Beers. Research Committee Chairperson: Luis Martí-Bonmatí. Members: Marco Essig, Thomas Helbich, Celso Matos, Wiro Niessen, Anwar Padhani, Harriet C. Thoeny, Siegfried Trattnig, Jean-Paul Vallée. Co-opted members: Peter Brader, Nicolas Grenier) on behalf of the European Society of Radiology (ESR) and with the help of Sabrina Doblas, INSERM U773, Paris, France.

It was approved by the ESR Executive Council in December 2012..

According to ESR: “There is increasing interest in developing the quantitative imaging of biomarkers in personalised medicine”. In this perspective, “Biomarkers” are tissue properties that can be quantitatively and reproducibly measured by imaging devices. One example for a major unmet need, which I found to be most interesting is the imaging-based detection of tumor invasiveness.

Quoting from the paper: ” Biomarkers are defined as “characteristics that are objectively measured and evaluated as indicators of normal biological processes, pathological processes, or pharmaceutical responses to a therapeutic intervention” [1]. Broadly, biomarkers fall into two categories: bio-specimen biomarkers, including molecular biomarkers and genetic biomarkers, and bio-signal biomarkers or imaging biomarkers. Bio-specimen biomarkers are obtained by removing a sample from a patient. Examples of these molecular biomarkers are genes and proteins detected from fluids or tissue samples. Bio-signal biomarkers remove no material from the patient, but rather detect and analyse an electromagnetic, photonic or acoustic signal emitted by the patient [2]. These imaging biomarkers have the advantage of being non-invasive, spatially resolved and repeatable [3]. They are of particular interest if they can overcome the limitations of the established histological “gold standards”. Indeed, invasive reference examinations, such as biopsy, can be inconclusive, are non-representative of the whole tissue (which is a tremendous limitation when assessing malignant tumours, which are known to be heterogeneous) and possess non-negligible levels of mortality and morbidity.

Genetic biomarkers indicate whether a disease may occur, but they are usually inefficient to assess the presence and stage of a disease. Similar to molecular biomarkers, imaging biomarkers can be used for early detection of diseases, staging and grading, and predicting or assessing the response to treatment [3]. Accordingly, because of their relative lower cost compared with imaging, molecular biomarkers may be more appropriate for disease screening and early detection than imaging biomarkers. With their high sensitivity, molecular biomarkers could also detect subclinical stages of disease before any morphological or functional change is detectable on imaging. In contrast, imaging biomarkers are often more useful than molecular biomarkers for disease staging, and also grading and for assessing tumour response, because localised information is crucial.

The main messages ESR wishes to deliver in this paper are that:

• Using imaging-biomarkers to streamline drug discovery and disease progression will drive a huge advancement in healthcare.

• The clinical qualification and validation of imaging biomarkers technology pose challenges, mainly in establishing the accuracy and reproducibility of such techniques. In that respect, agreements on standards and evaluation methods (e.g. clinical studies design) is imperative.

• There should be high motivation to pursue the development of imaging-biomarkers as the “clinical value of new biomarkers is of the highest priority in terms of patient management, assessing risk factors and disease prognosis.”

The paper deals to a great extent with the requirements on accuracy, reproducibility, standardization and quality control from the process of developing imaging-biomarkers:

Accuracy: Before being routinely used in the clinic, imaging biomarkers must be validated. Determining the accuracy implies calculating the sensitivity and specificity of the biomarker when compared with a biological process, such as tumour necrosis, which can be assessed at histopathological examination… [69]  [10, 11]

Reproducibility: Repeatability (measurements at short intervals on the same subjects using the same equipment in the same centres) and reproducibility (measurements at short intervals on the same subjects using different facilities in the same and different centres) studies must be conducted for image acquisition and image analysis…. Reproducibility studies are now very often included in scientific papers, as advised by the “standards for reporting of diagnostic accuracy” (STARD) criteria and should ideally include Bland-Altman plots and results of coefficients of repeatability [1617].

Standardisation: Standardisation relates to the establishment of norms or requirements about technical aspects. In the development of imaging biomarkers, two main aspects should be considered: Standardisation of image acquisition and Standardisation of image analysis…  [18] [1921]  [22] [27, 28] [3133]

Quality control: Adequate phantoms could be used to validate, on a day-to-day basis, that the biomarker stays robust and to avoid any drift in the machine, acquisition or processing protocol….  [34] [3035] [36] [37] [23].

The proposed development workflow:

“Similar to new drugs, the development of biomarkers has to pass along a pipeline going from discovery, through verification in different laboratories, validation and qualification before they can be used in clinical routine. Validation includes the determination of the accuracy and the precision (reproducibility) of the biomarker and standardisation concerns both acquisition and analysis. Qualification, defined as a “graded, fit-for-purpose evidentiary process linking a biomarker with biological processes and clinical end-points”, is a validation process in large cohorts of patients involving multiple centres, similar to phase III clinical trials, to obtain regulatory approval as surrogate endpoints [4]. A more extensive path to biomarker development has been reported [5]. The first step is the proof of concept, which defines any specific change relevant to the disease that can be studied using the available imaging and computational techniques. The relationship between this change and the presence, grading and response to treatment of the disease constitutes the proof of mechanism. The images needed to extract the biomarker must be appropriate (in terms of resolution, signal and contrast behaviour). Preparation of images relates to improving the data before the analysis (such as segmentation, filtering, interpolation or registration). The analysis and modelling of the signal by computational numerical adjustment of a mathematical model allow extracting the needed information (such as structural, physical, chemical, biological and functional properties). After this voxel-by-voxel computation, the spatial distribution of the biomarker can be depicted by parametric images, defined as derived secondary images which pixels represent the distribution values of a given parameter. Multivariate parametric images obtained by statistical modelling of the relevant parameters allow the reduction of data and a clear definition of the defined disease target. The abnormal values should be defined and measured through histogram analysis. A pilot test on a small sample of subjects, with and without the disease, has to be performed to validate the process—also called proof of principle—and to evaluate the influence of potential variations related to age, sex or any other source of biases. Finally, proofs of efficacy and effectiveness on larger and well-defined series of patients will show the ability of a biomarker to measure the clinical endpoint (Fig. 1).

Steps for the development of imaging biomarkers (adapted from [5])

Steps for the development of imaging biomarkers (adapted from [5])

The authors admit that the requirement posed on development of imaging-biomarkers represents a huge challenge and they try to offer ideas, mainly taken from the “MRI experience” to overcome certain hurdles. There is one important point on which they do not discuss: the definition of appropriate reference test. It is my own experience, based on many study protocols I developed in the past decade, that without reaching an agreement on that point, the development of imaging-biomarkers will just move in circles. Note, that today’s most “acceptable” reference test is histopathology, which everyone admits (as well mentioned in this paper); suffers many limitations. When it comes to validating imaging-biomarkers, the need to accurately match imaging products with histopathology is an additional major hurdle.

This is why, I see as a necessary step, to develop “real-time” imaging based tissue characterization combined with in-situ imaging-based histology.

 

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Author and Curator: Dror Nir, PhD

The management of men with prostate cancer is becoming one of the most challenging public health issues in the Western world. It is characterized by: over-diagnosis; over-treatment; low treatment efficacy; treatment related toxicity; escalating cost; and unsustainability [Bangma et al, 2007; Esserman et al, 2009]. How come? Well, everyone accepts that most prostate cancers are clinically insignificant. It is well known that all men above 65 harbor some sort of prostate cancer. Due to the current aggressive PSA-based screening, one in six men will be diagnosed with prostate cancer. Yet, the lifetime risk of dying of prostate cancer is only 3%. The problem is that, once diagnosed with prostate cancer, there is no accurate tool to identify those men that will die of the disease (in my previous post I mentioned 1:37). Currently, screening practices for prostate cancer are relying on the very unspecific prostate-specific-antigen (PSA) bio-marker test to determine which men are at higher risk of harboring prostate cancer and therefore need a biopsy. The existing diagnostic test is a transrectal ultrasound (TRUS) guided prostate biopsy aimed at extracting representative tissue from areas where cancer usually resides. This procedure suffers from several obvious faults:

1. Since the imaging tool used (B-mode ultrasound) is poor at detecting malignancies in the prostate, the probability of hitting a clinically significant cancer or missing a clinically insignificant cancer is subject to random error.

2. TRUS biopsy is also subjected to systematic error as it misses large parts of the prostate which might harbor cancer (e.g. apex and anterior zones).
3. TRUS guided biopsies are often unrepresentative of the true burden of cancer as either the volume or grade of cancer can be underestimated.

In the last ten years I was leading the development of an innovative ultrasound-based technology, HistoScanningTM, aimed at improving the aforementioned faults;

Among the other most popular imaging modalities aimed at better prostate cancer detection in routine use are: MRIElastography, Contrast Enhanced Ultrasound etc…

In my future posts I will go into more detail on how these imaging modalities fit into routine workflow, how much they stay within budget constraints and what level of promise they bear for promoting personalized medicine. Stay tuned… Footnote: According to the final report by an advisory panel to the USA government: Doctors should no longer offer the PSA prostate cancer screening test to healthy men because they’re more likely to be harmed by the blood draw, and the chain of medical interventions that often follows than be helped; (http://www.usatoday.com/news/health/story/2012-05-21/prostate-cancer-screening-test-harmful/55118036/1) But then; what should be offered instead?

Other posts on this Scientific Website addressing Prostate Cancer

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

https://pharmaceuticalintelligence.com/2012/07/31/prostate-cancers-plunged-after-uspstf-guidance-will-it-happen-again/

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

https://pharmaceuticalintelligence.com/2012/05/27/new-prostate-cancer-screening-guidelines-face-a-tough-sell-study-suggests/

ROLE OF VIRAL INFECTION IN PROSTATE CANCER

https://pharmaceuticalintelligence.com/2012/09/01/role-of-viral-infection-in-prostate-cancer/

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