Cited by 515Radiomics and Machine Learning in Medical ImagingMathematical Biology Tumor GrowthMedical Imaging Techniques and Applications

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Quantitative Imaging in Cancer Evolution and Ecology

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Gatenby, Robert A., Grove, Olya, Gillies, Robert J.·2013·Radiology

DOI 10.1148/radiol.13122697PMC PMC3781355

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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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Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886
Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing
Gerlinger, M., Rowan, A. J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., Martinez, P., Matthews, N., Stewart, A., Tarpey, P., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1113205 · Cited by 7,774
The Clonal Evolution of Tumor Cell Populations: Acquired genetic lability permits stepwise selection of variant sublines and underlies tumor progression.
Nowell, P. C. (1976) · Science · doi:10.1126/science.959840 · Cited by 6,471
Radiomics: extracting more information from medical images using advanced feature analysis
Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., et al. (2012) · European Journal of Cancer · doi:10.1016/j.ejca.2011.11.036 · Cited by 6,084
Self-citation
Clonal evolution in cancer
Greaves, M., & Maley, C. C. (2012) · Nature · doi:10.1038/nature10762 · Cited by 3,162
Distant metastasis occurs late during the genetic evolution of pancreatic cancer
Yachida, S., Jones, S., Bozic, I., Antal, T., Leary, R., Fu, B., Kamiyama, M., Hruban, R. H., Eshleman, J. R., Nowak, M. A., et al. (2010) · Nature · doi:10.1038/nature09515 · Cited by 2,502
Uses and misuses of bioclimatic envelope modeling
Araújo, M. B., & Peterson, A. T. (2012) · Ecology · doi:10.1890/11-1930.1 · Cited by 1,112
A microenvironmental model of carcinogenesis
Gatenby, R. A., & Gillies, R. J. (2007) · Nature reviews. Cancer · doi:10.1038/nrc2255 · Cited by 741
Self-citation
Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer
Tixier, F., Rest, C. C. L., Hatt, M., Albarghach, N. M., Pradier, O., Metges, J., Corcos, L., & Visvikis, D. (2011) · Journal of Nuclear Medicine · doi:10.2967/jnumed.110.082404 · Cited by 695
Preparing for Precision Medicine
Mirnezami, R., Nicholson, J. K., & Darzi, A. W. (2012) · New England Journal of Medicine · doi:10.1056/nejmp1114866 · Cited by 663

Show all 51 references

Research works printed but not linked: 6

10.1023/A:1022944019665 · DOI
Patel LR, Cancer Lett
Pang KK, 2003, J Chin Med Assoc, 66, 655
Ahmed A, J Magn Reson Imaging
Yang X, 2011, J Biomed Biotechnol, 2011, 732848
Zagdanski AM, 1994, AJNR Am J Neuroradiol, 15, 1359

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Authors: Gatenby, R. A., Grove, O., & Gillies, R. J.
Affiliations: Moffitt Cancer Center (US)
ORCID: Gatenby, Robert A.
Gillies, Robert J.
Venue: Radiology
Publisher: Radiological Society of North America (RSNA)
Published: 2013-10
Volume: 269
Issue: 1
Pages: 8-14
ISSN: 0033-8419 (ISSN-L)
1527-1315
DOI: 10.1148/radiol.13122697
PMC: PMC3781355
OpenAlex: W2124735791
Cited by: 515
References: 51
Citations by year: 2026: 40 · 2025: 62 · 2024: 52 · 2023: 33 · 2022: 40 · 2021: 49 · 2020: 42 · 2019: 46 · 2018: 44 · 2017: 33 · 2016: 34 · 2015: 23 · 2014: 13 · 2013: 4
FWCI: 3.50
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Radiology (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: National Cancer Institute (U54CA143970, R01CA125627, U01CA143062, R01CA170595, R01CA077575)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Physical Sciences › Mathematics › Modeling and Simulation › Mathematical Biology Tumor Growth
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
Keywords: tumor heterogeneity, tumor evolution, Darwinian selection, tumor microenvironment, tumor perfusion, tumor necrosis
MeSH terms: Diagnostic Imaging, Ecology, Humans, Models, Genetic, Neoplasms, Genetic Predisposition to Disease, Precision Medicine

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