Advertisements
Feeds:
Posts
Comments

Posts Tagged ‘CT’


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).

P1a

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

P1b

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). 

p1e

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

p1f

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).

p2

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.

 

p3

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).

p4

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).

p5a

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. 

p5b

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).

References

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

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

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

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

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

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

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

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

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

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

    1. Gerlinger M,
    2. Swanton C

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

    1. Kern SE

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

    1. Nowell PC

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

    1. Greaves M,
    2. Maley CC

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

    1. Vincent TL,
    2. Brown JS

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

    1. Gatenby RA,
    2. Gillies RJ

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

    1. Bowers MA,
    2. Matter SF

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

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

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

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

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

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

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

    1. Jaffe CC

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

    1. Burton A

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

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

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

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

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

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

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

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

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

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

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

    1. Pang KK,
    2. Hughes T

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

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

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

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

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

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

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

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

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

    1. Rubin DL

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

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

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

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

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

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

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

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

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

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

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

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

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

    1. Yang X,
    2. Knopp MV

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    1. Araújo MB,
    2. Peterson AT

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

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

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

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

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

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

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

Advertisements

Read Full Post »


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?

Read Full Post »


Causes and imaging features of false positives and false negatives on 18F-PET/CT in oncologic imaging

 Reporter: Dror Nir, PhD

Early this year I have posted on: Whole-body imaging as cancer screening tool; answering an unmet clinical need? F-PET/CT was discussed in this post as a leading modality in that respect. Here I report on an article dedicated to the sources for misdiagnosis; i.e. false negatives and false positives when applying this technology:

Causes and imaging features of false positives and false negatives on 18F-PET/CT in oncologic imaging, Niamh M. Long and Clare S. Smith /Insights into Imaging© European Society of Radiology 201010.1007/s13244-010-0062-3

Abstract

Background

18F-FDG is a glucose analogue that is taken up by a wide range of malignancies. 18F-FDG PET-CT is now firmly established as an accurate method for the staging and restaging of various cancers. However, 18F-FDG also accumulates in normal tissue and other non-malignant conditions, and some malignancies do not take up F18-FDG or have a low affinity for the tracer, leading to false-positive and false-negative interpretations.

Methods

PET-CT allows for the correlation of two separate imaging modalities, combining both morphological and metabolic information. We should use the CT to help interpret the PET findings. In this article we will highlight specific false-negative and false-positive findings that one should be aware of when interpreting oncology scans.

Results

We aim to highlight post-treatment conditions that are encountered routinely on restaging scans that can lead to false-positive interpretations. We will emphasise the importance of using the CT component to help recognise these entities to allow improved diagnostic accuracy.

Conclusion

In light of the increased use of PET-CT, it is important that nuclear medicine physicians and radiologists be aware of these conditions and correlate the PET and CT components to avoid misdiagnosis, over staging of disease and unnecessary biopsies.

Introduction

[18F] 2-fluoro-2deoxy-D-glucose (18F-FDG) PET-CT imaging has become firmly established as an excellent clinical tool in the diagnosis, staging and restaging of cancer. 18F-FDG (a glucose analog) is taken up by cells via glucose transporter proteins. The glucose analog then undergoes phosphorylation by hexokinase to FDG-6 phosphate. Unlike glucose, FDG-phosphate does not undergo further metabolism and so becomes trapped in the cell as the cell membrane is impermeable to FDG-6 phosphate following phosphorylation [1].

Malignant tumors have a higher metabolic rate and generally express higher numbers of specific membrane transporter proteins than normal cells. This results in increased uptake of 18F-FDG by tumor cells and forms the basis of FDG-PET imaging [2]. Glucose however acts as a basic energy substrate for many tissues, and so 18F-FDG activity can be seen both physiologically and in benign conditions. In addition, not all tumors take up FDG [35]. The challenge for the interpreting physician is to recognize these entities and avoid the many pitfalls associated with 18F-FDG PET-CT imaging.

In this article we discuss false-positive and false-negative 18F-FDG PET-CT findings, common and atypical physiological sites of FDG uptake, and benign pathological causes of FDG uptake. We will focus on post-treatment conditions that can result in false-positive findings. We will highlight the importance of utilizing the CT component of the study, not only for attenuation correction but also in the interpretation of the study. The CT component of 18F-FDG PET-CT imaging can provide high-resolution anatomical information, which enables more accurate staging and assessment. For the purposes of this article, we refer to the descriptive terms “false-positive” and “false-negative” findings in the context of oncology imaging.

The authors acknowledge that there are recognized causes of FDG uptake that are not related to malignancy; however in this paper we refer to false-positive findings as FDG uptake that is not tumor related.

Patient preparation

Tumor uptake of FDG is reduced in the presence of raised serum glucose as glucose competes with FDG for uptake by the membrane transporter proteins. In order to prevent false-negative results, it is necessary for the patient to fast for at least 4–6 h prior to the procedure [6]. Induction of a euglycamic hypoinsulinaemic state also serves to reduce the uptake of glucose by the myocardium and skeletal muscle. In the fasting state, the decreased availability of glucose results in predominant metabolism of fatty acids by the myocardium. This reduces the intensity of myocardial uptake and prevents masking of metastatic disease within the mediastinum [6].

The radiotracer is administered intravenously (dose dependent on both the count rate capability of the system used and the patient’s weight), and the patient is left resting in a comfortable position during the uptake phase (60–90 min). Patient discomfort and anxiety can result in increased uptake in skeletal muscles of the neck and paravertebral regions. Muscular contraction immediately prior to or following injection can result in increased FDG activity in major muscle groups [6].

Patients are placed in a warm, quiet room with little stimulation, as speech during the uptake phase is associated with increased FDG uptake in the laryngeal muscles [7].

At our institution we perform the CT component with arms up except for head and neck studies where the arms are placed down by the side. This minimizes artifacts on CT. Depending on the type of cancer, oral contrast to label the bowel and intravenous contrast may also be given. The CT is performed with a full dose similar to a diagnostic CT, and lungs are analyzed following reconstruction with a lung algorithm. The PET scan is performed with 3–4 min per bed position; however the time per bed position will vary in different centers depending on both the dose of FDG administered and the specifications of the camera used for image acquisition. It is beyond the scope of this article to provide detailed procedure guidelines for 18F-FDG PET-CT imaging, and for this purpose we refer the reader to a comprehensive paper by Boellaard et al. [8].

Technical causes of false positives

Misregistration artifact

The evaluation of pulmonary nodules provides a unique challenge for combined PET-CT scanning due to differences in breathing patterns between CT and PET acquisition periods. CT imaging of the thorax is classically performed during a breath-hold; however PET images are acquired during tidal breathing, and this can contribute significantly to misregistration of pulmonary nodules on fused PET-CT images. Misregistration is particularly evident at the lung bases, which can lead to difficulty differentiating pulmonary nodules from focal liver lesions (Fig. 1) [9].

f1

Fig. 1

18F-FDG PET-CT performed in a 65-year-old male with colorectal cancer. On the coronal PET images, a focus of increased FDG uptake is seen at the right lung base (black arrow). Contrast CT does not show any pulmonary nodules but does demonstrate a liver metastasis in the superior aspect of the right lobe of the liver (yellow arrow)

Acquiring CT imaging of the thorax during quiet respiration can help to minimize misregistration artifacts. It is also important to correlate your PET and CT findings by scrolling up and down to make sure that lesions match.

Injected clot

A further diagnostic pitfall in staging of intrathoracic disease can be caused by injected clot. Injection of radioactive clot following blood withdrawal into the syringe at the time of radiotracer administration can result in pulmonary hotspots [10]. The absence of a CT correlate for a pulmonary hotspot should raise the possibility of injected clot; however this is a diagnosis of exclusion, and it is important to carefully evaluate the adjacent slices to ensure the increased radiotracer activity does not relate to misregistration of a pulmonary nodule or hilar lymph node. The area of abnormal radiotracer uptake should also be closely evaluated on subsequent restaging CT to ensure there has been no interval development of an anatomical abnormality in the region of previously diagnosed injected clot (Fig. 2) [11].

f2

Fig. 2

18F-FDG PET-CT performed in a 28-year-old male with an osteosarcoma of the femur. A focus of increased FDG uptake (yellow arrow) is identified in the left lower lobe with no CT correlate (a). A 3-month follow-up CT thorax again does not demonstrate any pulmonary nodules confirming that the uptake seen originally on the PET-CT was due to injected clot (b)

Injection artifact

Leakage of radiotracer into the subcutaneous tissues at the injection site or tissued injection can result in subcutaneous tracking of FDG along lymphatic channels in the arm. This can result in spurious uptake in axillary nodes distal to the injection site [12]. Careful attention must be paid to the technical aspects of the study to ensure accurate staging. Injection at the side contralateral to the site of disease is advised where feasible to allow differentiation between artifactual and metastatic uptake, particularly in breast cancer patients. The side of injection should also be clearly documented during administration of radiotracer, and this information should be available to the reader in order to ensure pathological FDG uptake is not spuriously attributed to injection artifact (Fig. 3).

f3

Fig. 3

18F-FDG PET-CT performed in a 56-year-old woman with colorectal cancer. Some low grade FDG uptake is identified in non-enlarged right axillary nodes (yellow arrow) consistent with injection artifact

Imaging of metallic implants

The use of CT for attenuation correction negates the need for traditional transmission attenuation correction, reducing scanning time. There are however technical factors relating to the use of CT imaging for attenuation correction, which lead to artefacts when imaging metal [9]. The presence of metal implants in the body produces streak artifact on CT imaging and degrades image quality. When CT images are used for attenuation correction, the presence of metal results in over attenuation of PET activity in this region and can result in artifactual ‘hot spots.’ Metal prostheses, dental fillings, indwelling ports and breast expanders and sometimes contrast media are common causes of streak artifact secondary to high photon absorption and can cause attenuation correction artifacts [9]. In order to avoid false positives, particularly when imaging metallic implants careful attenuation should be paid to the nonattenuation corrected images, which do not produce this artifact.

Sites of physiological FDG uptake

Physiological uptake in a number of organs is readily recognized and rarely confused with malignancy. These include cerebral tissue, the urinary system, liver and spleen. Approximately 20% of administered activity is renally excreted in the 2 h post-injection resulting in intense radiotracer activity in the renal collecting systems, ureters and bladder [13]. In order to minimize the intensity of renal activity, patients are advised to void prior to imaging. Moderate physiological FDG uptake is noted in the liver, spleen, GI tract and salivary glands. Uptake in the cecum and right colon tends to be higher than in the remainder of the colon due to the presence of glucose-avid lymphocytes [14].

Other sites of physiological FDG activity can be confused with malignancy. Examples include activity within brown fat, adrenal activity, uterus and ovaries.

Brown fat

FDG uptake in hyper-metabolic brown adipose tissue is well recognized as a potential source of false positive in 18F-FDG PET-CT imaging. The incidence of FDG uptake in brown fat has been reported as between 2.5–4% [1516].

Hypermetabolic brown fat is more commonly identified in children than in adults and is more prevalent in females than in males. It occurs more frequently in patients with low body mass index and in cold weather [15].

Glucose accumulation within brown fat is increased by sympathetic stimulation as brown fat is innervated by the sympathetic nervous system. In view of this, administration of oral propranolol is advised by some authors as it has been shown to reduce the uptake of FDG by brown fat [17]. This is not performed at our institution; however, attempts are made to reduce FDG uptake in brown fat by maintaining a warm ambient temperature and providing patients with blankets during the uptake phase.

The typical distribution of brown fat in a bilateral symmetric pattern in the supraclavicular and neck regions is rarely confused with malignancy. In cases where hypermetabolic brown fat is seen to surround lymph nodes, the CT images should be separately evaluated to allow morphological assessment of the lymph nodes. The classical CT features of pathological replacement of lymph nodes should be sought, namely increased short axis diameter, loss of the fatty hilum and loss of the normal concavity of the lymph node. If the morphology of the lymph node is entirely normal, malignancy can be confidently excluded and the increased uptake attributed to brown fat [18].

Atypical brown fat in the mediastinum can be misinterpreted as nodal metastases and has been identified in the paratracheal, paraoesophageal, prevascular regions, along the pericardium and in the interatrial septum. Extramediastinal sites of brown fat uptake include the paravertebral regions, perinephric, perihepatic and subdiaphragmatic regions and in the intraatrial septum [16].

The absence of an anatomical lesion on CT imaging in areas of FDG uptake should raise the possibility of brown fat to the reader. Careful evaluation of the CT images must be performed to confirm the presence of adipose tissue in the anatomical region correlating to the increased FDG activity on 18F-FDG PET before this activity be attributed to brown fat.

An awareness of the possibility of brown fat in atypical locations is vital to avoid overstaging, and correlation with CT imaging increases reader confidence in differentiating brown fat from malignancy (Fig. 4).

f4

Fig. 4

18F-FDG PET-CT surveillance scan performed in a 36-year-old male with a history of seminoma. Symmetrical uptake is noted in the neck, supraclavicular fossa and paravertebral regions consistent with typical appearance of brown fat activity (black arrow). Brown fat uptake is also seen in the left supradiaphragmatic region and left paraoesophageal region (yellow arrow) (a). 18F-FDG PET-CT performed in a 48-year-old male with a history of colorectal cancer. Increased FDG uptake is noted within brown fat associated with lipomatous hypertrophy of the intra-atrial septum (b)

Uterine and ovarian uptake

In premenopausal women endometrial uptake of FDG varies cyclically and is increased both at ovulation and during the menstrual phase of the cycle with mean SUV values of 3.5–5 [19]. Endometrial uptake in postmenopausal women is abnormal and warrants further investigation; however benign explanations for increased FDG uptake include recent curettage, uterine fibroids and endometrial polyps [19].

Benign ovarian uptake of FDG in premenopausal women can be associated with ovulation. In postmenopausal women, ovarian uptake of FDG should be further investigated (Fig. 5).

f5

Fig. 5

18F-FDG PET-CT performed in a 42-year-old premenopausal female with breast cancer. She was scanned during menstruation. FDG uptake is noted within metastatic right axillary nodes (black arrow). Increased FDG uptake is also noted within the endometrial canal of the uterus (yellow arrow), which is thickened on CT, consistent with active menstruation (a). 18F-FDG PET-CT performed in the same 42-year-old woman at a different stage in her menstrual cycle showing resolution of the previously identified uterine uptake (yellow arrow) (b)

Adrenal uptake

18F-FDG PET imaging is commonly used for evaluation of adrenal masses in patients with diagnosed malignancies. Similarly incidental adrenal lesions are commonly identified on staging 18F-FDG PET-CT imaging. The positive predictive value of 18F-FDG PET-CT evaluation of adrenal lesions has been reported as high as 95% with a similarly high negative predictive value of 94% [20].

Causes of false-positive adrenal lesions include angiomyolipoma, adrenal hyperplasia and adrenal adenomas (up to 5%) [2124]. FDG activity greater than that of the liver is generally associated with malignancy; however benign lesions have been reported with greater activity than liver [21].

Evaluation of the CT component can provide additional diagnostic information with identification of HU attenuation values of <10 on noncontrast CT for adrenal adenomas or fat-containing myelolipomata [21].

Symmetrical intense FDG activity with no identifiable abnormality on CT is associated with benign physiological FDG uptake (Fig. 6).

f6 f6-b

Fig. 6

18F-FDG PET-CT performed in a 50-year-old woman with inflammatory breast cancer. Diffuse increased FDG uptake is noted within the right breast (yellow arrow) and in a right axillary node (black arrow), consistent with malignancy (a). Increased symmetrical uptake is also noted within both adrenal glands with no abnormal correlate on CT (yellow arrow) (b). Post-chemotherapy PET-CT performed 5 months later demonstrates resolution of the activity within the breast, increased uptake in the bone marrow consistent with post treatment effect (black arrow) and persistent increased uptake in the adrenal glands (yellow arrow), confirming benign physiological activity (c)

Thyroid uptake

Thyroid uptake is incidentally identified on 18F-FDG PET imaging with a frequency of almost 4%, with a diffuse uptake pattern in roughly half of cases and a focal pattern in the remainder [22]. The majority of diffuse uptake represents chronic thyroiditis, multinodular goiter or Graves’ disease, whereas focal uptake is associated with a risk of malignancy that ranges from 30.9–63.6% in published studies [2223]. Focal thyroid uptake requires further investigation with ultrasound and tissue biopsy.

Uptake in the gastrointestinal tract

The pattern of physiological uptake within the GI tract is highly variable. Low-grade linear uptake is likely related to smooth muscle activity and swallowed secretions. More focal increased uptake in the distal esophagus is sometimes seen with Barrett’s esophagus. In view of this, referral for OGD may be reasonable in cases of increased uptake in the distal esophagus [1424].

The typical pattern of FDG uptake in the stomach is of low-grade activity in a J-shaped configuration. Small bowel typically demonstrates mild heterogeneous uptake throughout. Common pitfalls of small bowel evaluation relate to spuriously high uptake in underdistened or overlapping loops of bowel [1425].

Within the colon, FDG uptake is highly variable, however can be quite avid particularly in the cecum, right colon and rectosigmoid regions. Focal areas of FDG activity within the colon that are of greater intensity than background liver uptake should raise the suspicion of a colonic neoplasm (Fig. 7) [2526].

f7

Fig. 7

18F-FDG PET-CT restaging scan performed in a 65-year-old female with a history of breast cancer. Incidental focal uptake is identified in the ascending colon where some abnormal thickening is seen on the CT component (yellow arrow). Colonoscopy confirmed the presence of a T3 adenocarcinoma

In a review of over 3,000 patients’ focal areas of abnormal FDG uptake within the gastrointestinal tract (GIT) were identified in 3% of cases of staging 18F-FDG PET-CT studies.

Incidental malignant lesions were identified in 19% of these patients with pre-malignant lesions including adenomas in 42% of the patients [27]. In view of this endoscopy referral is recommended in the absence of a clear benign correlate for focal areas of avid uptake on CT imaging.

Treatment-related causes of false-positive uptake

There are a number of conditions that can occur in patients undergoing treatment for cancer. When imaging these patients to assess for response, we often see these treatment-related conditions. It is important to recognize the imaging features to avoid misdiagnosis.

Thymus/thymic hyperplasia

Thymic hyperplasia post-chemotherapy is a well-described phenomenon. It is generally seen in children and young adults at a median of 12 months post chemotherapy [28]. The presence of increased FDG uptake in the anterior mediastinum can be attributed to thymic hyperplasia by identification of a triangular soft tissue density seen retrosternally on CT with a characteristic bilobed anatomical appearance [29]. In the presence of thymic hyperplasia, there is generally preservation of the normal shape of the gland despite an increase in size [30].

Superior mediastinal extension of thymic tissue is an anatomical variant that has been described in children and young adults (Fig. 8).

f8

Fig. 8

A 3.5-year-old boy with abdominal Burkitt’s lymphoma. Coronal 18F-FDG PET scan obtained 5 months after completion of treatment shows increased activity in the thymus in an inverted V configuration and in superior thymic extension (white arrow). Note physiologic activity within the right neck in the sternocleidomastoid muscle (a). Axial CT image from the same 18F-FDG PET-CT study performed 5 months after treatment shows a nodule (white arrow) anteromedial to the left brachiocephalic vein (b). Axial fusion image shows that the FDG activity in the superior mediastinum corresponds to this enlarged nodule anteromedial to left brachiocephalic vein (white arrow) (c). Axial fusion image shows increased activity in an enlarged thymus consistent with thymic hyperplasia (white arrow; standardized uptake value 3.0) of similar intensity to activity in superior mediastinum (d)

It presents as a soft tissue nodule anteromedial to the left brachiocephalic vein and represents a remnant of thymic tissue along the path of migration in fetal life. In patients with thymic hyperplasia, a superior mediastinal nodule in this location may represent accessory thymic tissue. An awareness of this physiological variant is necessary to prevent misdiagnosis [28].

G-CSF changes

Granulocyte colony-stimulating factor is a glycoprotein hormone that regulates proliferation and differentiation of granulocyte precursors. It is used to accelerate recovery from chemotherapy-related neutropaenia in cancer patients. Intense increased FDG uptake is commonly observed in the bone marrow and spleen following GCSF therapy; however the bone marrow response to GCSF can be differentiated from pathological infiltration by its intense homogeneous nature without focally increased areas of FDG uptake. Increased FDG uptake attributable to GCSF uptake rapidly decreases following completion of therapy and generally resolves within a month (Fig. 9).

f9

Fig. 9

18F-FDG PET-CT performed in a 46-year-old male post four cycles of chemotherapy for lymphoma and 2 weeks post administration of G-CSF. Note the diffuse homogeneous increased uptake throughout the bone marrow and the increased uptake in the spleen (yellow arrow)

Marked uptake in the bone marrow can also be seen following chemotherapy, reflecting marrow activation [3132].

Radiation pneumonitis

Inflammatory morphological changes in the radiation field post-irradiation of primary or metastatic lung tumor can result in false-positive diagnosis. Radiation pneumonitis typically occurs following high doses of external beam radiotherapy (>40 Gy). In the acute phase (1–8 weeks) radiation pneumonitis is characterized by ground-glass opacities and patchy consolidation. This can commonly lead to a misdiagnosis of infection. Chronic CT appearances of fibrosis and traction bronchiectasis in the radiation field allow correct interpretation of increased FDG uptake as radiation pneumonitis as opposed to disease recurrence [3334]. Other organs are also sensitive to radiation, and persistent uptake due to inflammatory change can persist for up to 1 year. It is important to elicit a history of radiation from the patient and to correlate the increased uptake with the CT findings to avoid missing a disease recurrence (Fig. 10).

f 10

Fig. 10

18F18-FDG PET-CT performed in a 52-year-old male with newly diagnosed esophageal carcinoma. Increased FDG uptake is identified within the esophagus (black arrow) and an upper abdominal lymph node (yellow arrow), consistent with malignancy (a). 18F18-FDG PET-CT performed 6 weeks post-completion of radiotherapy for esophageal carcinoma. Linear increased uptake is identified along the mediastinum in the radiation port (black arrow). This corresponds to areas of ground-glass change on CT (yellow arrow) consistent with acute radiation change (b)

Infection

Bone marrow suppression places chemotherapy patients at increased risk of infection.

Inflammatory cells such as neutrophils and activated macrophages at the site of infection or inflammation actively accumulate FDG [35].

In the post-therapy setting it has been reported that up to 40% of FDG uptake occurs in non-tumor tissue [12]. Infection is one of the most common causes of false-positive 18F-FDG PET-CT findings post-chemotherapy. Chemotherapy patients are susceptible to a wide variety of infections, including upper respiratory chest infections, pneumonia, colitis and cholecystitis. Reactivation of tuberculous infection can occur in immunocompromised patients post,chemotherapy, and correlation with CT imaging can prevent misdiagnosis in suspected cases.

Atypical infections such as cryptococcosis and pneumocystis can also present as false-positives on FDG imaging (Fig. 11) [36].

f 11

Fig. 11

18F-FDG PET-CT performed in a 57-year-old male 2 weeks following chemotherapy for lung cancer. Increased FDG uptake is noted within the cecum (black arrow). On CT there is some thickening of the cecal wall and stranding of the pericecal fat (yellow arrow) consistent with typhilits

Surgery and radiotherapy

There are inherent challenges in the interpretation of 18F-FDG PET-CT imaging in the postoperative patient. Non-tumor-related uptake of FDG is frequently identified in post-operative wound sites, at colostomy sites or at the site of post-radiation inflammatory change. 18F-FDG PET-CT imaging during the early postoperative/post-radiotherapy period may result in overstaging of patients because of non-neoplastic uptake of FDG [12]. Careful evaluation of the CT component in this setting is vital as CT imaging can provide valuable additional information regarding benign inflammatory conditions commonly encountered in the postoperative setting such as abscesses or wound infection. These conditions are often readily apparent on CT, particularly when oral and/or IV contrast CT is administered.

The reader should also bear in mind that avid uptake of FDG at postoperative/post radiotherapy sites may mask malignant FDG uptake in neighboring structures. In order to minimize non-tumoral uptake of FDG, it is advisable to allow at least 6 weeks post-surgery or completion of radiotherapy prior to performing staging 18F-FDG PET-CT [24].

Talc pleurodesis

Talc pleurodesis is a commonly performed procedure for the treatment of persistent pneumothorax or pleural effusion. The fibrotic/inflammatory reaction results in increased FDG uptake on 18F-FDG PET imaging with corresponding high-density areas of pleural thickening on CT. SUV values of between 2–16.3 have been seen years after the procedure [37].

When increased FDG uptake is indentified in the pleural space in a patient with a known history of pleurodesis, correlation with CT is recommended to detect pleural thickening of increased attenuation that suggests talc rather than tumor.

It is extremely important that a comprehensive history with relevant surgical interventions is available to the reader in order to ensure accurate diagnosis and staging (Fig. 12).

f 12

Fig. 12

18F-FDG PET-CT performed in a 69-year-old male with a history of non-Hodgkin’s lymphoma. The patient had a previous talc pleurodesis for a persistent left pleural effusion. Increased FDG activity is identified within the left pleura (black arrow). CT demonstrates a pleural effusion with high density material along the left pleural surface consistent with talc (yellow arrow)

Flare phenomenon

Bone healing is mediated by osteoblasts, and an early increase in osteoblast activity on successful treatment of metastatic disease has been described [38]. “Bone flare” refers to a disproportionate increase in bone lesion activity on isotope bone scan despite evidence of a therapeutic response to treatment in other lesions and has been well described in breast, prostate and lung tumors. ‘Flare phenomenon’ has also been described on 18F-FDG PET-CT in patients with lung and breast cancer who are receiving chemotherapy [39].

Differentiating between increased FDG uptake due to flare response and true disease progression may not be possible in the early post-treatment studies. While it is recognized that bone flare is a rare phenomenon, an increase in baseline skeletal activity and appearance of new bone lesions despite apparent response or stable disease elsewhere should be interpreted with caution to avoid erroneously suggesting progressive disease.

Osteonecrosis

Osteonecrosis or avascular necrosis has been well described as a complication of combination chemotherapy treatment, especially where it includes intermittent high-dose corticosteroids (e.g., lymphoma patients) [40]. Commonly encountered sites include the hip and less frequently the proximal humerus. Occasionally we can see a discrete entity known as jaw osteonecrosis. Patients receiving IV bisphosphonates for the management of bone metastases are at an increased risk of developing this [41]. The development of osteonecrosis in the mandible is frequently preceded by tooth extraction. Radiographic findings that may be visualized on CT include osteosclerosis, dense woven bone, thickened lamina dura and sub-periosteal bone deposition [42]. FDG uptake can be seen in areas of osteonecrosis (Fig. 13).

f 13

Fig. 13

18F-FDG PET-CT performed in a 46-year-old gentleman with a history of non-Hodgkin’s lymphoma. Increased FDG uptake is identified in the right proximal humerus (black arrow). CT of the area demonstrates a corresponding vague area of sclerosis (yellow arrow). Biopsy of the area yielded osteonecrosis with no evidence of metastatic disease

Insufficiency fractures

Pelvic insufficiency fractures have been described following irradiation for gynecological, colorectal, anal and prostate cancer. They commonly occur within 3–12 months post-radiation treatment, and osteoporosis is often a precipitating factor. FDG uptake in insufficiency fractures ranges from mild and diffuse to intense and heterogeneous. The maximum SUV values are variable with reported values of between 2.4–7.2 [43]. Differentiating insufficiency fractures from bone metastases can prove challenging; however they are often bilateral and occur in characteristic locations within the radiation field—sacral ala, pubic rami and iliac bones. Biopsy of insufficiency fractures can lead to irreparable damage and so careful correlation of 18F-FDG PET imaging with the CT component along with radiation history is vital for correct diagnosis. CT allows evaluation of the bone cortex and adjacent soft tissues, which can confirm the diagnosis of a pathological fracture or a metastatic deposit.

Follow-up of suspected insufficiency fractures demonstrates a reduction in FDG uptake over time (Fig. 14) [43].

f 14

Fig. 14

18F-FDG PET-CT performed in a 46-year-old female, 3 years post-chemo-radiation for cervical carcinoma. Low grade FDG uptake is identified in the left acetabulum and right pubic bone (black arrow). CT demonstrates pathological fractures in these areas consistent with insufficiency fractures (yellow arrow)

Sarcoidosis

Sarcoidosis is a chronic multisystem disorder characterized by non-caseating granulomas and derangement of normal tissue architecture [36]. Sarcoidosis has been reported in association with a variety of malignancies either synchronously or post-chemotherapy. Aggregation of inflammatory cells post-chemotherapy is associated with accumulation of FDG, and the intensity of FDG uptake may correlate with disease activity [36].

When suspected disease recurrence presents with signs and symptoms compatible with sarcoidosis (i.e., mediastinal and bihilar lymphadenopathy), this must be excluded by clinical, radiological and pathological correlation to prevent mistreatment (Fig. 15).

f 15

Fig. 15

18F-FDG PET-CT performed in a 67-year-old male for restaging of laryngeal carcinoma. Increased FDG uptake is noted in the left lower neck and left mediastinum (black arrow). CT demonstrates lymphadenopathy in these areas (yellow arrow), some of which are calcified. Biopsy of the left lower neck node confirmed sarcoidosis

FDG-PET negative tumors

There are a number of malignancies that can be FDG-PET negative. Examples include bronchoalveolar carcinoma and carcinoid tumors in the lung, renal cell carcinomas and hepatomas, mucinous tumors of the GIT and colon, and low grade lymphomas [34448]. Careful evaluation of the CT component of the study however will prevent a misdiagnosis (Fig. 16).

f 16

Fig. 16

18F-FDG PET-CT performed in a 52-year-old female with breast cancer and chronic hepatitis. On the CT component a hyper-enhancing mass is identified in segment 4 of the liver (yellow arrow). No increased FDG activity is identified in this area on the PET component. Biopsy of the mass confirmed the diagnosis of a hepatocellular carcinoma

Osteoblastic metastases

Bone metastases are diagnosed in up to 85% of patients with advanced breast cancer, leading to significant morbidity and mortality. Sclerotic bone metastases are commonly associated with breast carcinoma [49].18F-FDG PET imaging is superior to nuclear bone scan in detection of osteolytic breast metastases; however it commonly fails to diagnose osteoblastic or sclerotic metastases [50]. Review of bony windows on CT imaging allows identification of sclerotic metastases and ensures accurate staging of metastatic bone disease (Fig. 17).

f 17

Fig. 17

Staging 18F-FDG PET-CT performed in a 45-year-old female with newly diagnosed breast cancer. CT demonstrates multiple small sclerotic foci in the spine and pelvis (yellow arrow), consistent with bony metastases. These are FDG negative on the PET component of the study

Discussion/conclusion

18F-FDG PET imaging has dramatically changed cancer staging, and findings of restaging studies commonly effect changes in treatment protocols. 18F-FDG however is not tumor specific. As interpreting physicians we need to be aware of these false positives and false negatives. In this review we have outlined atypical physiological sites of FDG uptake along with common causes of FDG uptake in benign pathological conditions, many of which are treatment related. With 18F-FDG PET-CT we have the advantage of two imaging modalities. The PET component gives us functional information and the CT, anatomical data. We have discussed the importance of dual-modality imaging and correlation with CT imaging of the above conditions. Furthermore CT imaging provides important diagnostic information in evaluation of tumors that poorly concentrate FDG. In light of the increased reliance of 18F-FDG PET-CT for cancer staging, it is vital that radiologists and nuclear medicine physicians be aware of pitfalls in 18F-FDG PET-CT imaging and correlate PET and CT components to avoid misdiagnosis, overstaging of disease and unnecessary biopsies.

Other research papers related to the use of 18F-PET in management of cancer were published on this Scientific Web site:

State of the art in oncologic imaging of Lymphoma.

State of the art in oncologic imaging of Colorectal cancers.

State of the art in oncologic imaging of Prostate.

State of the art in oncologic imaging of lungs.

State of the art in oncologic imaging of breast.

Whole-body imaging as cancer screening tool; answering an unmet clinical need?

 

References

1.

Pauwels EK, Ribeiro MJ, Stoot JH et al (1998) FDG accumulation and tumor biology. Nucl Med Biol 25:317–322PubMedCrossRef

2.

Wahl RL (1996) Targeting glucose transporters for tumor imaging: “sweet” idea, “sour” result. J Nucl Med 37(6):1038–1041PubMed

3.

Kim BT, Kim Y, Lee KS, Yoon SB, Cheon EM, Kwon OJ, Rhee CH, Han J, Shin MH (1998) Localized form of bronchioalveolar carcinoma: FDG PET findings. AJR 170(4):935–939PubMed

4.

Hoh CK, Hawkins RA, Glaspy JA, Dahlbom M, Tse NY, Hoffman EJ, Schiepers C, Choi Y, Rege S, Nitzsche E (1993) Cancer detection with whole-body PET using 2-[18F]fluoro-2-deoxy-D-glucose. J Comput Assist Tomogr 17(4):582–589PubMedCrossRef

5.

Fenchel S, Grab D, Nuessle K, Kotzerke J, Rieber A, Kreienberg R, Brambs HJ, Reske SN (2002) Asymptomatic adnexal masses: correlation of FDG PET and histopathologic findings. Radiology 223(3):780–788PubMedCrossRef

6.

Shreve PD, Anzai Y, Wahl RL (1999) Pitfalls in oncologic diagnosis with FDG PET imaging: physiologic and benign variants. Radiographics 19(1):61–77, quiz 150–151PubMed

7.

Abouzied MM, Crawford ES, Nabi HA (2005) 18 F-FDG imaging: pitfalls and artifacts. J Nucl Med Technol 33(3):145–155PubMed

8.

Boellaard R, O’Doherty MJ, Weber WA, Mottaghy FM, Lonsdale MN, Stroobants SG, Oyen WJ, Kotzerke J, Hoekstra OS, Pruim J, Marsden PK, Tatsch K, Hoekstra CJ, Visser EP, Arends B, Verzijlbergen FJ, Zijlstra JM, Comans EF, Lammertsma AA, Paans AM, Willemsen AT, Beyer T, Bockisch A, Schaefer-Prokop C, Delbeke D, Baum RP, Chiti A, Krause BJ (2010) FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0. Eur J Nucl Med Mol Imaging 37(1):181–200PubMedCrossRef

9.

Sureshbabu W, Mawlawi O (2005) PET/CT imaging artifacts. J Nucl Med Technol 33(3):156–161, quiz 163–164PubMed

10.

Lin E, Alavi A (2009) PET and PET/CT: A Clinical Guide: 2nd Edn. Thieme New York p 145

11.

Hany TF, Heuberger J, von Schulthess GK (2003) Iatrogenic FDG foci in the lungs: a pitfall of PET image interpretation. Eur Radiol 13(9):2122–2127, Epub 2002 Oct 17PubMedCrossRef

12.

Kazama T, Faria SC, Varavithya V, Phongkitkarun S, Ito H, Macapinlac HA (2005) FDG PET in the evaluation of treatment for lymphoma: clinical usefulness and pitfalls. Radiographics 25(1):191–207PubMedCrossRef

13.

Swanson DP, Chilton HM, Thrall JH (1990) Pharmaceuticals in medical imaging. Macmillan, New York

14.

Prabhakar HB, Sahani DV, Fischman AJ, Mueller PR, Blake MA (2007) Bowel hot spots at PET-CT. Radiographics 27(1):145–159PubMedCrossRef

15.

Yeung HW, Grewal RK, Gonen M, Schöder H, Larson SM (2003) Patterns of (18)F-FDG uptake in adipose tissue and muscle: a potential source of false-positives for PET. J Nucl Med 44(11):1789–1796PubMed

16.

Truong MT, Erasmus JJ, Munden RF, Marom EM, Sabloff BS, Gladish GW, Podoloff DA, Macapinlac HA (2004) Focal FDG uptake in mediastinal brown fat mimicking malignancy: a potential pitfall resolved on PET/CT. Am J Roentgenol 183(4):1127–1132

17.

Söderlund V, Larsson SA, Jacobsson H (2007) Reduction of FDG uptake in brown adipose tissue in clinical patients by a single dose of propranolol. Eur J Nucl Med Mol Imaging 34(7):1018–1022PubMedCrossRef

18.

Sumi M, Ohki M, Nakamura T (2001) Comparison of sonography and CT for differentiating benign from malignant cervical lymph nodes in patients with squamous cell carcinoma of the head and neck. AJR 176(4):1019–1024PubMed

19.

Lerman H, Metser U, Grisaru D, Fishman A, Lievshitz G, Even-Sapir E (2004) Normal and abnormal 18 F-FDG endometrial and ovarian uptake in pre- and postmenopausal patients: assessment by PET/CT. J Nucl Med 45(2):266–271PubMed

20.

Lu Y, Xie D, Huang W, Gong H, Yu J (2010) 18 F-FDG PET/CT in the evaluation of adrenal masses in lung cancer patients. Neoplasma 57(2):129–134PubMedCrossRef

21.

Boland GW, Blake MA, Holalkere NS, Hahn PF (2009) PET/CT for the characterization of adrenal masses in patients with cancer: qualitative versus quantitative accuracy in 150 consecutive patients. AJR Am J Roentgenol 192(4):956–962PubMedCrossRef

22.

Chen W, Parsons M, Torigian DA, Zhuang H, Alavi A (2009) Evaluation of thyroid FDG uptake incidentally identified on FDG-PET/CT imaging. Nucl Med Commun 30(3):240–244PubMedCrossRef

23.

Choi JY, Lee KS, Kim HJ, Shim YM, Kwon OJ, Park K, Baek CH, Chung JH, Lee KH, Kim BT (2006) Focal thyroid lesions incidentally identified by integrated 18 F-FDG PET/CT: clinical significance and improved characterization. J Nucl Med 47(4):609–615PubMed

24.

Blake MA, Slattery J, Sahani DV, Kalra MK (2005) Practical issues in abdominal PET/CT. Appl Radiol 34(11):8–18

25.

Kei PL, Vikram R, Yeung HW, Stroehlein JR, Macapinlac HA (2010) Incidental finding of focal FDG uptake in the bowel during PET/CT: CT features and correlation with histopathologic results. AJR Am J Roentgenol 194(5):W401–W406PubMedCrossRef

26.

Pandit-Taskar N, Schöder H, Gonen M, Larson SM, Yeung HW (2004) Clinical significance of unexplained abnormal focal FDG uptake in the abdomen during whole-body PET. AJR Am J Roentgenol 183(4):1143–1147PubMed

27.

Kamel EM, Thumshirn M, Truninger K, Schiesser M, Fried M, Padberg B, Schneiter D, Stoeckli SJ, von Schulthess GK, Stumpe KD (2004) Significance of incidental 18 F-FDG accumulations in the gastrointestinal tract in PET/CT: correlation with endoscopic and histopathologic results. J Nucl Med 45(11):1804–1810PubMed

28.

Smith CS, Schöder H, Yeung HW (2007) Thymic extension in the superior mediastinum in patients with thymic hyperplasia: potential cause of false-positive findings on 18 F-FDG PET/CT. AJR Am J Roentgenol 188(6):1716–1721PubMedCrossRef

29.

Ferdinand B, Gupta P, Kramer EL (2004) Spectrum of thymic uptake at 18 F-FDG PET. Radiographics 24(6):1611–1616PubMedCrossRef

30.

Baron RL, Lee JK, Sagel SS, Levitt RG (1982) Computed tomography of the abnormal thymus. Radiology 142(1):127–134PubMed

31.

Hollinger EF, Alibazoglu H, Ali A, Green A, Lamonica G (1998) Hematopoietic cytokine-mediated FDG uptake simulates the appearance of diffuse metastatic disease on whole-body PET imaging. Clin Nucl Med 23(2):93–98PubMedCrossRef

32.

Kazama T, Swanston N, Podoloff DA, Macapinlac HA (2005) Effect of colony-stimulating factor and conventional- or high-dose chemotherapy on FDG uptake in bone marrow. Eur J Nucl Med Mol Imaging 32(12):1406–1411PubMedCrossRef

33.

Claude L, Pérol D, Ginestet C, Falchero L, Arpin D, Vincent M, Martel I, Hominal S, Cordier JF, Carrie C (2004) A prospective study on radiation pneumonitis following conformal radiation therapy in non-small-cell lung cancer: clinical and dosimetric factors analysis. Radiother Oncol 71(2):175–181PubMedCrossRef

34.

Frank A, Lefkowitz D, Jaeger S, Gobar L, Sunderland J, Gupta N, Scott W, Mailliard J, Lynch H, Bishop J et al (1995) Decision logic for retreatment of asymptomatic lung cancer recurrence based on positron emission tomography findings. Int J Radiat Oncol Biol Phys 32(5):1495–1512PubMedCrossRef

35.

Love C, Tomas MB, Tronco GG, Palestro CJ (2005) FDG PET of infection and inflammation. Radiographics 25(5):1357–1368PubMedCrossRef

36.

Chang JM, Lee HJ, Goo JM, Lee HY, Lee JJ, Chung JK, Im JG (2006) False positive and false negative FDG-PET scans in various thoracic diseases. Korean J Radiol 7(1):57–69PubMedCrossRef

37.

Kwek BH, Aquino SL, Fischman AJ (2004) Fluorodeoxyglucose positron emission tomography and CT after talc pleurodesis. Chest 125(6):2356–2360PubMedCrossRef

38.

Coleman RE, Mashiter G, Whitaker KB, Moss DW, Rubens RD, Fogelman I (1988) Bone scan flare predicts successful systemic therapy for bone metastases. J Nucl Med 29(8):1354–1359PubMed

39.

Krupitskaya Y, Eslamy HK, Nguyen DD, Kumar A, Wakelee HA (2009) Osteoblastic Bone Flare on F18-FDG PET in Non-small Cell Lung Cancer (NSCLC) Patients Receiving Bevacizumab in addition to standard Chemotherapy. J Thorac Oncol 4(3):429–431PubMedCrossRef

40.

Talamo G, Angtuaco E, Walker RC, Dong L, Miceli MH, Zangari M, Tricot G, Barlogie B, Anaissie E (2005) Avascular necrosis of femoral and/or humeral heads in multiple myeloma: results of a prospective study of patients treated with dexamethasone-based regimens and high-dose chemotherapy. J Clin Oncol 23(22):5217–5223PubMedCrossRef

41.

Catalano L, Del Vecchio S, Petruzziello F, Fonti R, Salvatore B, Martorelli C, Califano C, Caparrotti G, Segreto S, Pace L, Rotoli B (2007) Sestamibi and FDG-PET scans to support diagnosis of jaw osteonecrosis. Ann Hematol 86(6):415–423PubMedCrossRef

42.

Arce K, Assael LA, Weissman JL, Markiewicz MR (2009) MR imaging findings in bisphosphonate-related osteonecrosis of jaws. J Oral Maxillofac Surg 67(5 Suppl):75–84PubMedCrossRef

43.

Oh D, Huh SJ, Lee SJ, Kwon JW (2009) Variation in FDG uptake on PET in patients with radiation-induced pelvic insufficiency fractures: a review of 10 cases. Ann Nucl Med 23(6):511–516PubMedCrossRef

44.

Erasmus JJ, McAdams HP, Patz EF Jr, Coleman RE, Ahuja V, Goodman PC (1998) Evaluation of primary pulmonary carcinoid tumors using FDG PET. AJR Am J Roentgenol 170(5):1369–1373PubMed

45.

Kang DE, White RL Jr, Zuger JH, Sasser HC, Teigland CM (2004) Clinical use of fluorodeoxyglucose F 18 positron emission tomography for detection of renal cell carcinoma. J Urol 171(5):1806–1809PubMedCrossRef

46.

Khan MA, Combs CS, Brunt EM, Lowe VJ, Wolverson MK, Solomon H, Collins BT, Di Bisceglie AM (2000) Positron emission tomography scanning in the evaluation of hepatocellular carcinoma. J Hepatol 32(5):792–797PubMedCrossRef

47.

Berger KL, Nicholson SA, Dehdashti F, Siegel BA (2000) FDG PET evaluation of mucinous neoplasms: correlation of FDG uptake with histopathologic features. AJR Am J Roentgenol 174(4):1005–1008PubMed

48.

Jerusalem G, Beguin Y, Najjar F, Hustinx R, Fassotte MF, Rigo P, Fillet G (2001) Positron emission tomography (PET) with 18 F-fluorodeoxyglucose (18 F-FDG) for the staging of low-grade non-Hodgkin’s lymphoma (NHL). Ann Oncol 12(6):825–830PubMedCrossRef

49.

Tateishi U, Gamez C, Dawood S, Yeung HW, Cristofanilli M, Macapinlac HA (2008) Bone metastases in patients with metastatic breast cancer: morphologic and metabolic monitoring of response to systemic therapy with integrated PET/CT. Radiology 247(1):189–196PubMedCrossRef

50.

Huyge V, Garcia C, Vanderstappen A, Alexiou J, Gil T, Flamen P (2009) Progressive osteoblastic bone metastases in breast cancer negative on FDG-PET. Clin Nucl Med 34(7):417–420PubMedCrossRef

Read Full Post »


State of the art in oncologic imaging of lungs.

Author-Writer: Dror Nir, PhD

 This is the second post in a series in which I will address the state of the art in oncologic imaging based on a review paper; Advances in oncologic imaging that provides updates on the latest approaches to imaging of 5 common cancers: breast, lung, prostate, colorectal cancers, and lymphoma. This paper is published at CA Cancer J Clin 2012. © 2012 American Cancer Society.

The paper gives a fair description of the use of imaging in interventional oncology based on literature review of more than 200 peer-reviewed publications.

In this post I summaries the chapter on lung cancer imaging.

Lung Cancer Imaging

“Lung cancer remains the most common cause of death from cancer worldwide, having resulted in 1.38 million deaths (18.2% of all cancer deaths) in 2008.48 It also represents the leading cause of death in smokers and the leading cause of cancer mortality in men and women in the United States. In 2012, it was estimated that 226,160 new cases of lung cancer would be diagnosed (accounting for about 14% of cancer diagnoses) and that lung cancer would cause 160,340 deaths (about 29% of cancer deaths in men and 26% of cancer deaths in women) in the United States.1 The 1-year relative survival rate for the disease increased from 35% to 43% from 1975 through 1979 to 2003 through 2006.49 The 5-year survival rate is 53% for disease that is localized when first detected, but only 15% of lung cancers are diagnosed at this early stage.”

For cancer with such poor survival rates removal of the primary lesion by surgery at an early-stage disease is the best option. The current perception in regards to lung cancr is that patients may have subclinical disease for years before presentation. It is also known that early lung cancer lesions; adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) are slow-growing, doubling time which can exceed 2 years.52 But, since at present, no lung cancer early-detection biomarker is clinically available, the diagnosis of this disease is primarily based on symptoms, and detection often occurs after curative intervention and when it’s already too late – see: Update on biomarkers for the detection of lung cancer and also Diagnosing lung cancer in exhaled breath using gold nanoparticles. Until biomarker is found, the burden of screening for this disease is on imaging.

“AIS and MIA generally appear as a single peripheral ground-glass nodule on CT. A small solid component may be present if areas of alveolar collapse or fibroblastic proliferation are present,5051 but any solid component should raise concern for a more invasive lesion (Fig. 8). Growth over time on imaging can often be difficult to assess due to the long doubling time of these AIS and MIA, which can exceed 2 years.52 However, indicators other than growth, such as air bronchograms, increasing density, and pleural retraction within a ground-glass nodule are suggestive of AIS or MIA.

CT image shows a ground glass nodule, which is the typical appearance of AIS, in the right upper lobe.

CT image shows a ground glass nodule, which is the typical appearance of AIS, in the right upper lobe.

 

CT (A) demonstrated extensive consolidation with air bronchograms in the left upper lobe, which at surgical resection were found to represent adenocarcinoma of mixed subtype with predominate (70%) mucinous bronchioloalveolar subtype. PET imaging in the same patient (B) demonstrated uptake in the lingula higher than expected for bronchioloalveolar carcinoma and probably due to secondary inflammation/infection. CT (C) obtained 3 years after images (A) and (B) demonstrated biopsy-proven recurrent soft-tissue mass near surgical site. Fused FDG/PET images (D) demonstrate no uptake in the area. This finding is consistent with the decreased uptake usually seen in tumors of bronchioloalveolar histology (new terminology of MIA).

CT (A) demonstrated extensive consolidation with air bronchograms in the left upper lobe, which at surgical resection were found to represent adenocarcinoma of mixed subtype with predominate (70%) mucinous bronchioloalveolar subtype. PET imaging in the same patient (B) demonstrated uptake in the lingula higher than expected for bronchioloalveolar carcinoma and probably due to secondary inflammation/infection. CT (C) obtained 3 years after images (A) and (B) demonstrated biopsy-proven recurrent soft-tissue mass near surgical site. Fused FDG/PET images (D) demonstrate no uptake in the area. This finding is consistent with the decreased uptake usually seen in tumors of bronchioloalveolar histology (new terminology of MIA).

In August 2011 the results of the “National Lung Screening Trial “ which was funded by the National Cancer Institute (NCI) were published in NEJM; Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening. This randomized study results showed that with low-dose CT screening of high-risk persons, there was a significant reduction of 20% in the mortality rate from lung cancer as compared to chest radiographs screening.

Based on these results one can find the following information regarding Lung Cancer Screening on the NCI web-site:

Three screening tests have been studied to see if they decrease the risk of dying from lung cancer.

The following screening tests have been studied to see if they decrease the risk of dying from lung cancer:

  • Chest x-ray: An x-ray of the organs and bones inside the chest. An x-ray is a type of energy beam that can go through the body and onto film, making a picture of areas inside the body.
  • Sputum cytology: Sputum cytology is a procedure in which a sample of sputum (mucus that is coughed up from the lungs) is viewed under a microscope to check for cancer cells.
  • Low-dose spiral CT scan (LDCT scan): A procedure that uses low-dose radiation to make a series of very detailed pictures of areas inside the body. It uses an x-ray machine that scans the body in a spiral path. The pictures are made by a computer linked to the x-ray machine. This procedure is also called a low-dose helical CT scan.

Screening with low-dose spiral CT scans has been shown to decrease the risk of dying from lung cancer in heavy smokers.

A lung cancer screening trial studied people aged 55 years to 74 years who had smoked at least 1 pack of cigarettes per day for 30 years or more. Heavy smokers who had quit smoking within the past 15 years were also studied. The trial used chest x-rays or low-dose spiral CT scans (LDCT) scans to check for signs of lung cancer.

LDCT scans were better than chest x-rays at finding early-stage lung cancer. Screening with LDCT also decreased the risk of dying from lung cancer in current and former heavy smokers.

Guide is available for patients and doctors to learn more about the benefits and harms of low-dose helical CT screening for lung cancer.

Screening with chest x-rays or sputum cytology does not decrease the risk of dying from lung cancer.

Chest x-ray and sputum cytology are two screening tests that have been used to check for signs of lung cancer. Screening with chest x-ray, sputum cytology, or both of these tests does not decrease the risk of dying from lung cancer.

The authors of Advances in oncologic imaging found out that for pre-treatment staging and post treatment follow-up of lung cancer patients mainly involves CT (preferably contrast enhanced, FDG PET and PET/CT. “Integrated PET/CT has been found to be more accurate than PET alone, CT alone, or visual correlation of PET and CT for staging NSCLC (Non-small-cell lung carcinoma).59 “

The standard treatment of choice for localized disease remains surgical resection with or without chemo-radiation therapy (stage dependant). “The current recommendations for routine follow-up after complete resection of NSCLC are as follows: for 2 years following surgery a contrast-enhanced chest CT scan every 4 to 6 months and then yearly non-contrast chest CT scans.62 Detection of recurrence on CT is the primary goal in the initial years, and therefore, optimally, a contrast-enhanced scan should be obtained to evaluate the mediastinum. In subsequent years, when identifying an early second primary lung cancer becomes of more clinical importance, a non-contrast CT chest scan suffices to evaluate the lung parenchyma.

CT (A) of 78-year-old male who was status post–left lobe lobectomy and left upper lobe wedge resection shows recurrent nodule at the surgical resection site. Fused PET/CT (B) demonstrates increased [18F]FDG uptake in the corresponding nodule at the surgical resection site consistent with recurrent tumor.

CT (A) of 78-year-old male who was status post–left lobe lobectomy and left upper lobe wedge resection shows recurrent nodule at the surgical resection site. Fused PET/CT (B) demonstrates increased [18F]FDG uptake in the corresponding nodule at the surgical resection site consistent with recurrent tumor.

In patients undergoing chemotherapies: “ [18F]FDG PET response correlates with histologic response.63 [18F]FDG PET scan data can provide an early readout of response to chemotherapy in patients with advanced-stage lung cancer.64

In patients treated by recently developed “Targeted Therapies” such as Radiofrequency ablation (RFA) the authors found out that PET/CT is the preferred imaging modality for post treatment follow-up.

“ Most patients treated with pulmonary ablation will have had a pre-procedure CT or a fusion PET/CT scan, which allows more precise anatomic localization of abnormalities seen on PET. Generally, either CT or PET/CT is performed within a few weeks of the procedure to provide a new baseline to which future images can be compared to assess for changes in size, degree of enhancement or [18F]FDG avidity.67

CT (A) demonstrates new left upper lobe mass representing new primary NSCLC in a patient who had a status post–right pneumonectomy for a prior NSCLC. CT (B) obtained in the same patient 2 weeks after radiofrequency ablation (RFA) demonstrates the postablation density in the left upper lobe. Fused PET/CT (C) obtained 4 months after RFA demonstrates mild [18F]FDG uptake at RFA site in the left upper lobe consistent with posttreatment inflammation. Fused PET/CT (D) obtained 7 months after RFA demonstrates new focal [18F]FDG uptake at post-RFA-opacity consistent with recurrent tumor.

CT (A) demonstrates new left upper lobe mass representing new primary NSCLC in a patient who had a status post–right pneumonectomy for a prior NSCLC. CT (B) obtained in the same patient 2 weeks after radiofrequency ablation (RFA) demonstrates the postablation density in the left upper lobe. Fused PET/CT (C) obtained 4 months after RFA demonstrates mild [18F]FDG uptake at RFA site in the left upper lobe consistent with posttreatment inflammation. Fused PET/CT (D) obtained 7 months after RFA demonstrates new focal [18F]FDG uptake at post-RFA-opacity consistent with recurrent tumor.

Prostate Cancer Imaging

To be followed…

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

Diagnosing lung cancer in exhaled breath using gold nanoparticles

Lung Cancer (NSCLC), drug administration and nanotechnology

Non-small Cell Lung Cancer drugs – where does the Future lie?

Comprehensive Genomic Characterization of Squamous Cell Lung Cancers

Read Full Post »


Reporter: Aviral Vatsa PhD, MBBS

A new study in JBMR highlights a novel glucocorticoid receptor modulator Compound A (CpdA) with the potential for an improved risk/benefit profile. They tested the effects of CpdA on bone in a mouse model of GC‐induced bone loss.

This study underlines the bone‐sparing potential of CpdA and suggests that by preventing increases in the RANKL/OPG ratio or DKK‐1 in osteoblast lineage cells, GC‐induced bone loss may be ameliorated. © 2012 American Society for Bone and Mineral Research.

RESULTS

PRED reduced the total and trabecular bone density in the femur by 9% and 24% and in the spine by 11% and 20%, respectively, whereas CpdA did not influence these parameters. Histomorphometry confirmed these results and further showed that the mineral apposition rate was decreased by PRED whereas the number of osteoclasts was increased. Decreased bone formation was paralleled by a decline in serum P1NP, reduced skeletal expression of osteoblast markers, and increased serum levels of the osteoblast inhibitor dickkopf‐1 (DKK‐1). In addition, serum CTX‐1 and the skeletal RANKL/OPG ratio were increased by PRED. None of these effects were observed with CpdA. Consistent with the in vivo data, CpdA did not increase the RANKL/OPG ratio in MLO‐Y4 cells. Finally, CpdA also failed to transactivate DKK‐1 expression in bone tissue, BMSCs and osteocytes.

METHODS

Bone loss was induced in FVB/N mice by implanting slow‐release pellets containing either vehicle, prednisolone (PRED) (3.5 mg), or CpdA (3.5 mg). After 4 weeks, mice were killed to examine the effects on the skeleton using quantitative computed tomography, bone histomorphometry, serum markers of bone turnover, and gene expression analysis. To assess the underlying mechanisms, in vitro studies were performed with human bone marrow stromal cells (BMSCs) and murine osteocyte‐like cells (MLO‐Y4 cells).

Read Full Post »