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Quantitative Imaging in Cancer Evolution and Ecology
Open accessAbstract
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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“In this approach, images with different acquisition parameters (eg, contrast material–enhanced T1-weighted MR imaging, diffusion-weighted, and fluid attenuation sequences) can be combined to yield regions with specific combinations of quantitative image data.”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [36]
Citations
Cites
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
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-citationGreaves, 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-citationTixier, 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
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
Cited by
Sorted by citations
Items 501–513 of 513
Bao, W., Chen, X., Long, K., Shen, M., Li, S., Tan, C., & Chen, Z. (2026) · Research Square · doi:10.21203/rs.3.rs-10153558/v1
Xiong, H., 邹, 凤., Chen, J., Ai, G., Qiao, X., Wu, Y., Gu, X., & He, X. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.08.104
Chen, X., Huang, Q., Li, R., Cao, L.-Q., Gong, K., Wang, N., Ashby, C. R., Gu, J., & Chen, Z. (2026) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2026.109126
Song, D., Li, B., Liu, Y., Guo, P., Mi, Y., Yang, B., Qu, H., Jiang, Y., Song, Y., Zhang, C., et al. (2026) · JCO Precision Oncology · doi:10.1200/po-26-00243
Application advances of habitat imaging based on CT or PET/CT: a systematic review
Chen, Q., 任铁柱, Peng, Y., Ma, L., Xu, M., & Zhang, W. (2026) · La radiologia medica · doi:10.1007/s11547-026-02292-z
Ji, Y., Wang, J., Zhou, M., Wang, Y., Dai, Z., & Zheng, J. (2026) · JCO Precision Oncology · doi:10.1200/po-26-00609
Ultrasound-Based Habitat Radiomics for Differentiating Phyllodes Tumor from Breast Fibroadenoma
Xue, Q., Ding, W., Lu, Z., Chen, F., Jia, M. X., Liu, J., Sun, J., Li, S., Zhou, J., Zhao, C., et al. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.09.010
Wang, L.-N., Zhou, S.-Q., Li, F., Wu, L., Xu, L., Wang, K., Ge, N., Chen, Y., Peng, T., Song, T.-Q., et al. (2026) · Liver Cancer · doi:10.1159/lic/admag009
Lu, Y., Liang, B., Zeng, X., Zhang, S., Chen, Z., Chen, J., & Liao, H. (2026) · Research Square · doi:10.21203/rs.3.rs-10588615/v1
Kim, H., Seo, B.-S., Ryu, W., Choi, K., Kim, J., & Park, M. (2026) · Stroke Vascular and Interventional Neurology · doi:10.1161/svin.126.002570
Li, T.-X., Zou, T., Li, Z., Liu, Z.-L., Chen, J.-C., Xiong, Y.-L., Li, M.-X., Zhang, X.-Q., He, P., Guo, W.-R., et al. (2026) · iScience · doi:10.1016/j.isci.2026.117782
Non-Contrast CT Habitat Radiomics to Differentiate Parotid Pleomorphic Adenoma From Warthin Tumour
Li, Y., zhang, X., Tian, Y., Geng, G., 吴亚东, & Song, J. (2026) · International Dental Journal · doi:10.1016/j.identj.2026.111267
Wu, Y., Wang, H., Liu, C., Liu, M., Yi, H., Yu, N., & Jin, B. (2026) · European Journal of Radiology · doi:10.1016/j.ejrad.2026.113317
Metadata
- Authors
- Gatenby, R. A., Grove, O., & Gillies, R. J.
- Affiliations
- Moffitt Cancer Center (US)
- Venue
- Radiology
- Published
- 2013-10
- Volume
- 269
- Issue
- 1
- Pages
- 8-14
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- 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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