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Rosenkrantz AB, Mendiratta-Lala M, Bartholmai BJ, Ganeshan D, Abramson RG, Burton KR, Yu JPJ, Scalzetti EM, Yankeelov TE, Subramaniam RM, Lenchik L. Clinical utility of quantitative imaging. Acad Radiol 2015; 22:33-49. [PMID: 25442800 PMCID: PMC4259826 DOI: 10.1016/j.acra.2014.08.011] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2014] [Revised: 08/25/2014] [Accepted: 08/25/2014] [Indexed: 12/24/2022]
Abstract
Quantitative imaging (QI) is increasingly applied in modern radiology practice, assisting in the clinical assessment of many patients and providing a source of biomarkers for a spectrum of diseases. QI is commonly used to inform patient diagnosis or prognosis, determine the choice of therapy, or monitor therapy response. Because most radiologists will likely implement some QI tools to meet the patient care needs of their referring clinicians, it is important for all radiologists to become familiar with the strengths and limitations of QI. The Association of University Radiologists Radiology Research Alliance Quantitative Imaging Task Force has explored the clinical application of QI and summarizes its work in this review. We provide an overview of the clinical use of QI by discussing QI tools that are currently used in clinical practice, clinical applications of these tools, approaches to reporting of QI, and challenges to implementing QI. It is hoped that these insights will help radiologists recognize the tangible benefits of QI to their patients, their referring clinicians, and their own radiology practice.
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Affiliation(s)
- Andrew B Rosenkrantz
- Department of Radiology, NYU Langone Medical Center, 550 First Avenue, New York, NY 10016.
| | - Mishal Mendiratta-Lala
- Henry Ford Hospital, Abdominal and Cross-sectional Interventional Radiology, Detroit, Michigan
| | - Brian J Bartholmai
- Division of Radiology Informatics, Mayo Clinic in Rochester, Rochester, Minnesota
| | | | - Richard G Abramson
- Department of Radiology and Radiological Sciences, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Kirsteen R Burton
- Department of Medical Imaging and Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - John-Paul J Yu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
| | - Ernest M Scalzetti
- Department of Radiology, SUNY Upstate Medical University, Syracuse New York
| | - Thomas E Yankeelov
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee
| | - Rathan M Subramaniam
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins School of Medicine, and Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Leon Lenchik
- Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina
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Abramson RG, Burton KR, Yu JPJ, Scalzetti EM, Yankeelov TE, Rosenkrantz AB, Mendiratta-Lala M, Bartholmai BJ, Ganeshan D, Lenchik L, Subramaniam RM. Methods and challenges in quantitative imaging biomarker development. Acad Radiol 2015; 22:25-32. [PMID: 25481515 PMCID: PMC4258641 DOI: 10.1016/j.acra.2014.09.001] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Revised: 09/03/2014] [Accepted: 09/03/2014] [Indexed: 12/18/2022]
Abstract
Academic radiology is poised to play an important role in the development and implementation of quantitative imaging (QI) tools. This article, drafted by the Association of University Radiologists Radiology Research Alliance Quantitative Imaging Task Force, reviews current issues in QI biomarker research. We discuss motivations for advancing QI, define key terms, present a framework for QI biomarker research, and outline challenges in QI biomarker development. We conclude by describing where QI research and development is currently taking place and discussing the paramount role of academic radiology in this rapidly evolving field.
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Affiliation(s)
- Richard G. Abramson
- Department of Radiology and Radiological Sciences Vanderbilt University 1161 21 Ave. S, CCC-1121 MCN Nashville, TN 37232-2675 (615)322-6759 Fax (615) 322-3764
| | - Kirsteen R. Burton
- Dept. of Medical Imaging and Institute of Health Policy, Management and Evaluation University of Toronto 263 McCaul Street, 4th Floor Toronto, ON M5T1W7 (416) 978-6801
| | - John-Paul J. Yu
- Department of Radiology and Biomedical Imaging University of California, San Francisco 505 Parnassus Ave., M-391 Box 0628 San Francisco, CA 94143-0628
| | - Ernest M. Scalzetti
- Department of Radiology SUNY Upstate Medical University 750 E. Adams St. Syracuse NY 13210
| | - Thomas E. Yankeelov
- Institute of Imaging Science Vanderbilt University 1161 21 Ave. S, AA-1105 MCN Nashville, TN 37232-2310
| | - Andrew B. Rosenkrantz
- Department of Radiology NYU Langone Medical Center 550 First Avenue New York, NY 10016 (212) 263-0232 fax: (212) 263-6634
| | - Mishal Mendiratta-Lala
- Abdominal and Cross-sectional Interventional Radiology Henry Ford Hospital 2799 West Grand Blvd. Detroit, MI 48202 (313) 461-1648
| | - Brian J. Bartholmai
- Chair, Division of Radiology Informatics Mayo Clinic Rochester, MN Phone 507-284-4292 FAX: 507-284-8996
| | - Dhakshinamoorthy Ganeshan
- Department of Abdominal Imaging University of Texas MD Anderson Cancer Center Houston, TX 77030 713-792-2486 Fax: 713-745-1151
| | - Leon Lenchik
- Department of Radiology Wake Forest School of Medicine Medical Center Boulevard Winston-Salem, NC 27157 Phone: 336-716-4316 Fax: 336-716-1278
| | - Rathan M. Subramaniam
- Russell H Morgan Department of Radiology and Radiological Sciences Johns Hopkins School of Medicine Department of Health Policy and Management Johns Hopkins Bloomberg School of Public Health Johns Hopkins University Baltimore, MD
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53
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Sunderland JJ, Christian PE. Quantitative PET/CT scanner performance characterization based upon the society of nuclear medicine and molecular imaging clinical trials network oncology clinical simulator phantom. J Nucl Med 2014; 56:145-52. [PMID: 25525180 DOI: 10.2967/jnumed.114.148056] [Citation(s) in RCA: 94] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
UNLABELLED The Clinical Trials Network (CTN) of the Society of Nuclear Medicine and Molecular Imaging (SNMMI) operates a PET/CT phantom imaging program using the CTN's oncology clinical simulator phantom, designed to validate scanners at sites that wish to participate in oncology clinical trials. Since its inception in 2008, the CTN has collected 406 well-characterized phantom datasets from 237 scanners at 170 imaging sites covering the spectrum of commercially available PET/CT systems. The combined and collated phantom data describe a global profile of quantitative performance and variability of PET/CT data used in both clinical practice and clinical trials. METHODS Individual sites filled and imaged the CTN oncology PET phantom according to detailed instructions. Standard clinical reconstructions were requested and submitted. The phantom itself contains uniform regions suitable for scanner calibration assessment, lung fields, and 6 hot spheric lesions with diameters ranging from 7 to 20 mm at a 4:1 contrast ratio with primary background. The CTN Phantom Imaging Core evaluated the quality of the phantom fill and imaging and measured background standardized uptake values to assess scanner calibration and maximum standardized uptake values of all 6 lesions to review quantitative performance. Scanner make-and-model-specific measurements were pooled and then subdivided by reconstruction to create scanner-specific quantitative profiles. RESULTS Different makes and models of scanners predictably demonstrated different quantitative performance profiles including, in some cases, small calibration bias. Differences in site-specific reconstruction parameters increased the quantitative variability among similar scanners, with postreconstruction smoothing filters being the most influential parameter. Quantitative assessment of this intrascanner variability over this large collection of phantom data gives, for the first time, estimates of reconstruction variance introduced into trials from allowing trial sites to use their preferred reconstruction methodologies. Predictably, time-of-flight-enabled scanners exhibited less size-based partial-volume bias than non-time-of-flight scanners. CONCLUSION The CTN scanner validation experience over the past 5 y has generated a rich, well-curated phantom dataset from which PET/CT make-and-model and reconstruction-dependent quantitative behaviors were characterized for the purposes of understanding and estimating scanner-based variances in clinical trials. These results should make it possible to identify and recommend make-and-model-specific reconstruction strategies to minimize measurement variability in cancer clinical trials.
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Affiliation(s)
- John J Sunderland
- Department of Radiology, Carver College of Medicine, University of Iowa, Iowa City, Iowa; and
| | - Paul E Christian
- Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
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Smith AD, Roda D, Yap TA. Strategies for modern biomarker and drug development in oncology. J Hematol Oncol 2014; 7:70. [PMID: 25277503 PMCID: PMC4189730 DOI: 10.1186/s13045-014-0070-8] [Citation(s) in RCA: 88] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2014] [Accepted: 09/21/2014] [Indexed: 02/08/2023] Open
Abstract
Technological advancements in the molecular characterization of cancers have enabled researchers to identify an increasing number of key molecular drivers of cancer progression. These discoveries have led to multiple novel anticancer therapeutics, and clinical benefit in selected patient populations. Despite this, the identification of clinically relevant predictive biomarkers of response continues to lag behind. In this review, we discuss strategies for the molecular characterization of cancers and the importance of biomarkers for the development of novel antitumor therapeutics. We also review critical successes and failures in oncology, and detail the lessons learnt, which may aid in the acceleration of anticancer drug development and biomarker discovery.
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Affiliation(s)
- Alan D Smith
- Drug Development Unit, Royal Marsden NHS Foundation Trust, Division of Clinical Studies, The Institute of Cancer Research, Downs Road, Sutton, Surrey, SM2 5PT, UK.
| | - Desam Roda
- Drug Development Unit, Royal Marsden NHS Foundation Trust, Division of Clinical Studies, The Institute of Cancer Research, Downs Road, Sutton, Surrey, SM2 5PT, UK.
| | - Timothy A Yap
- Drug Development Unit, Royal Marsden NHS Foundation Trust, Division of Clinical Studies, The Institute of Cancer Research, Downs Road, Sutton, Surrey, SM2 5PT, UK.
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55
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Translation in solid cancer: are size-based response criteria an anachronism? Clin Transl Oncol 2014; 17:1-10. [PMID: 25073600 DOI: 10.1007/s12094-014-1207-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Accepted: 07/09/2014] [Indexed: 12/19/2022]
Abstract
The purpose of translation is the development of effective medicinal products based on validated science. A parallel objective is to obtain marketing authorization for the translated product. Unfortunately, in solid cancer, these two objectives are not mutually consistent as evidenced by the contrast between major advances in science and the continuing dismal record of pharmaceutical productivity. If the problem is unrelated to science, then the process of translation may require a closer examination, namely, the criteria for regulatory approval. This realization is important because, in this context, the objective of translation is regulatory approval, and science does not passively translate into useful medicinal products. Today, in solid cancer, response criteria related to tumor size are less useful than during the earlier cytotoxic drugs era; advanced imaging and biomarkers now allow for tracking of the natural history of the disease in the laboratory and the clinic. Also, it is difficult to infer clinical benefit from tumor shrinkage since it is rarely sustained. Accordingly, size-based response criteria may represent an anachronism relative to translation in solid cancer and it may be appropriate to align preclinical and clinical effort and shift the focus to local invasion and metastasis. The shift from a cancer cell-centric model to a stroma centric model offers novel opportunities not only to interupt the natural history of the disease, but also to rethink the relevance of outdated criteria of clinical response. Current evidence favors the opinion that, in solid cancer, a different, broader, and contextual approach may lead to interventions that could delay local invasion and metastasis. All elements supporting this shift, especially advanced imaging, are in place.
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56
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Aerts HJWL, Velazquez ER, Leijenaar RTH, Parmar C, Grossmann P, Carvalho S, Cavalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014; 5:4006. [PMID: 24892406 PMCID: PMC4059926 DOI: 10.1038/ncomms5006] [Citation(s) in RCA: 2893] [Impact Index Per Article: 289.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2013] [Accepted: 04/29/2014] [Indexed: 11/09/2022] Open
Abstract
Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost. An individual tumour is often heterogeneous and its various features can be visualised noninvasively using medical imaging. Here, the authors analyse large computed tomography data sets using radiomic algorithms to identify heterogeneity, and find that some of these tumour features have prognostic value across cancer types.
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Affiliation(s)
- Hugo J W L Aerts
- 1] Department of Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, 6229ET Maastricht, The Netherlands [2] Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02215-5450, USA [3] Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02215-5450, USA [4] Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215-5450, USA [5]
| | - Emmanuel Rios Velazquez
- 1] Department of Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, 6229ET Maastricht, The Netherlands [2] Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02215-5450, USA [3]
| | - Ralph T H Leijenaar
- Department of Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, 6229ET Maastricht, The Netherlands
| | - Chintan Parmar
- 1] Department of Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, 6229ET Maastricht, The Netherlands [2] Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02215-5450, USA
| | - Patrick Grossmann
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02215-5450, USA
| | | | - Sara Cavalho
- Department of Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, 6229ET Maastricht, The Netherlands
| | - Johan Bussink
- Department of Radiation Oncology, Radboud University Medical Center Nijmegen, PB 9101, 6500HB Nijmegen, The Netherlands
| | - René Monshouwer
- Department of Radiation Oncology, Radboud University Medical Center Nijmegen, PB 9101, 6500HB Nijmegen, The Netherlands
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network and Medical Biophysics Department, University of Toronto, Toronto, Ontario, Canada M5G 1L7
| | - Derek Rietveld
- Department of Radiation Oncology, VU University Medical Center, 1081 HZ Amsterdam, The Netherlands
| | - Frank Hoebers
- Department of Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, 6229ET Maastricht, The Netherlands
| | - Michelle M Rietbergen
- Department of Otolaryngology/Head and Neck Surgery, VU University Medical Center, 1081 HZ Amsterdam, The Netherlands
| | - C René Leemans
- Department of Otolaryngology/Head and Neck Surgery, VU University Medical Center, 1081 HZ Amsterdam, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, 6229ET Maastricht, The Netherlands
| | - John Quackenbush
- Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215-5450, USA
| | - Robert J Gillies
- Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612, USA
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, 6229ET Maastricht, The Netherlands
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Fennessy FM, McKay RR, Beard CJ, Taplin ME, Tempany CM. Dynamic contrast-enhanced magnetic resonance imaging in prostate cancer clinical trials: potential roles and possible pitfalls. Transl Oncol 2014; 7:120-9. [PMID: 24772215 PMCID: PMC3998683 DOI: 10.1593/tlo.13922] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2013] [Revised: 03/04/2014] [Accepted: 03/06/2014] [Indexed: 12/21/2022] Open
Abstract
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) evaluates the tissue microvasculature and may have a role in assessing and predicting therapeutic response in prostate cancer (PCa). In this review, we review principles of DCE-MRI and present the potential quantitative information that can be obtained. We discuss how it may be used as a biomarker for treatment with antiangiogenic and antivascular agents and potentially identify patients with PCa who may benefit from this form of therapy. Likewise, DCE-MRI may play a role in assessing response to combined androgen deprivation therapy and radiation therapy and theoretically could be a prognostic biomarker in evaluating second-generation hormone therapies. We also address the challenges of using DCE-MRI in PCa clinical trials and discuss the difficulties with standardization of this methodology to allow for biomarker validation, with particular reference to PCa.
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Affiliation(s)
- Fiona M Fennessy
- Department of Radiology, Brigham and Women's Hospital, Boston, MA ; Department of Radiology, Dana-Farber Cancer Institute, Boston, MA
| | - Rana R McKay
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Clair J Beard
- Department of Radiation Oncology, Brigham and Women's Hospital, Boston, MA
| | - Mary-Ellen Taplin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Clare M Tempany
- Department of Radiology, Brigham and Women's Hospital, Boston, MA
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Errors in Quantitative Image Analysis due to Platform-Dependent Image Scaling. Transl Oncol 2014; 7:65-71. [PMID: 24772209 DOI: 10.1593/tlo.13811] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2013] [Revised: 02/07/2014] [Accepted: 02/10/2014] [Indexed: 12/13/2022] Open
Abstract
PURPOSE To evaluate the ability of various software (SW) tools used for quantitative image analysis to properly account for source-specific image scaling employed by magnetic resonance imaging manufacturers. METHODS A series of gadoteridol-doped distilled water solutions (0%, 0.5%, 1%, and 2% volume concentrations) was prepared for manual substitution into one (of three) phantom compartments to create "variable signal," whereas the other two compartments (containing mineral oil and 0.25% gadoteriol) were held unchanged. Pseudodynamic images were acquired over multiple series using four scanners such that the histogram of pixel intensities varied enough to provoke variable image scaling from series to series. Additional diffusion-weighted images were acquired of an ice-water phantom to generate scanner-specific apparent diffusion coefficient (ADC) maps. The resulting pseudodynamic images and ADC maps were analyzed by eight centers of the Quantitative Imaging Network using 16 different SW tools to measure compartment-specific region-of-interest intensity. RESULTS Images generated by one of the scanners appeared to have additional intensity scaling that was not accounted for by the majority of tested quantitative image analysis SW tools. Incorrect image scaling leads to intensity measurement bias near 100%, compared to nonscaled images. CONCLUSION Corrective actions for image scaling are suggested for manufacturers and quantitative imaging community.
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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.
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Affiliation(s)
- Robert A Gatenby
- Departments of Radiology and Cancer Imaging and Metabolism, Moffitt Cancer Center, 12902 Magnolia Dr, Tampa, FL 33612
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