The DOI matches its Crossref record; 50 of the 51 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Evolutionary Game Theory, Natural Selection, and Darwinian Dynamics
Vincent, T. L., & Brown, J. S. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9780511542633 · Cited by 649
Decoding global gene expression programs in liver cancer by noninvasive imaging
Segal, E., Sirlin, C. B., Ooi, C., Adler, A. S., Gollub, J., Chen, X., Chan, B. K., Matcuk, G. R., Barry, C. T., Chang, H. Y., et al. (2007) · Nature Biotechnology · doi:10.1038/nbt1306 · Cited by 629
Ganeshan, B., Panayiotou, E., Burnand, K., Dizdarevic, S., & Miles, K. A. (2011) · European Radiology · doi:10.1007/s00330-011-2319-8 · Cited by 504
Automatic tumor segmentation using knowledge-based techniques
Clark, M. C., Hall, L., Goldgof, D. B., Velthuizen, R. P., Murtagh, F. R., & Silbiger, M. S. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.700731 · Cited by 461
Identification of noninvasive imaging surrogates for brain tumor gene-expression modules
Diehn, M., Nardini, C., Wang, D. S., McGovern, S., Jayaraman, M., Liang, Y., Aldape, K., Cha, S., & Kuo, M. D. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0801279105 · Cited by 440
How Darwinian models inform therapeutic failure initiated by clonal heterogeneity in cancer medicine
Gerlinger, M., & Swanton, C. (2010) · British Journal of Cancer · doi:10.1038/sj.bjc.6605912 · Cited by 439
Goh, V., Ganeshan, B., Nathan, P., Juttla, J. K., Vinayan, A., & Miles, K. A. (2011) · Radiology · doi:10.1148/radiol.11110264 · Cited by 368
Measures of Response: RECIST, WHO, and New Alternatives
Jaffe, C. C. (2006) · Journal of Clinical Oncology · doi:10.1200/jco.2006.06.5599 · Cited by 302
Miles, K. A., Ganeshan, B., Griffiths, M. R., Young, R. C. D., & Chatwin, C. R. (2009) · Radiology · doi:10.1148/radiol.2502071879 · Cited by 253
Kern, S. E. (2012) · Cancer Research · doi:10.1158/0008-5472.can-12-3232 · Cited by 234
Metapopulation dynamics and spatial heterogeneity in cancer
González-Garcı́a, I., Solé, R. V., & Costa, J. C. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.202139299 · Cited by 201
Texture analysis in assessment and prediction of chemotherapy response in breast cancer
Ahmed, A., Gibbs, P., Pickles, M. D., & Turnbull, L. W. (2012) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.23971 · Cited by 189
Landscape Ecology of Mammals: Relationships between Density and Patch Size
Bowers, M. A., & Matter, S. F. (1997) · Journal of Mammalogy · doi:10.2307/1383044 · Cited by 183
Quantifying heterogeneity in human tumours using MRI and PET
Asselin, M., O’Connor, J. P. B., Boellaard, R., Thacker, N. A., & Jackson, A. (2012) · European Journal of Cancer · doi:10.1016/j.ejca.2011.12.025 · Cited by 158
Creating and Curating a Terminology for Radiology: Ontology Modeling and Analysis
Rubin, D. L. (2007) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-007-9073-0 · Cited by 154
Nair, V. S., Gevaert, O., Davidzon, G., Napel, S. A., Graves, E. E., Hoang, C. D., Shrager, J. B., Quon, A. S. W., Rubin, D. L., & Plevritis, S. K. (2012) · Cancer Research · doi:10.1158/0008-5472.can-11-3943 · Cited by 138
Quantifying spatial heterogeneity in dynamic contrast‐enhanced MRI parameter maps
Rose, C. J., Mills, S. J., O'Connor, J. P. B., Buonaccorsi, G. A., Roberts, C., Watson, Y., Cheung, S., Zhao, S., Whitcher, B., Jackson, A., et al. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.22003 · Cited by 125
Yang, X., & Knopp, M. V. (2011) · BioMed Research International · doi:10.1155/2011/732848 · Cited by 123
Frouge, C., RE, E.-M. G., Contesso, G., Paola, R. D., & Bl�ry, M. (1994) · Investigative Radiology · doi:10.1097/00004424-199412000-00006 · Cited by 122
Putting the “Ecology” into Environmental Flows: Ecological Dynamics and Demographic Modelling
Shenton, W., Bond, N. R., Yen, J. D. L., & Nally, R. M. (2012) · Environmental Management · doi:10.1007/s00267-012-9864-z · Cited by 102
Promise and pitfalls of quantitative imaging in oncology clinical trials
Kurland, B. F., Gerstner, E. R., Mountz, J. M., Schwartz, L. H., Ryan, C. W., Graham, M. M., Buatti, J. M., Fennessy, F. M., Eikman, E. A., Kumar, V., et al. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.06.009 · Cited by 101
Lloyd, M. C., Allam‐Nandyala, P., Purohit, C. N., Burke, N., Coppola, D., & Bui, M. M. (2010) · Journal of Pathology Informatics · doi:10.4103/2153-3539.74186 · Cited by 83
Variance of SUVs for FDG-PET/CT is Greater in Clinical Practice Than Under Ideal Study Settings
Kumar, V., Nath, K., Berman, C. G., Kim, J., Tanvetyanon, T., Chiappori, A. A., Gatenby, R. A., Gillies, R. J., & Eikman, E. A. (2013) · Clinical Nuclear Medicine · doi:10.1097/rlu.0b013e318279ffdf · Cited by 76
Self-citationBonnerot, V., CHARPENTIER, A., Frouin, F., Kalifa, C., Vanel, D., & Paola, R. D. (1992) · Investigative Radiology · doi:10.1097/00004424-199210000-00016 · Cited by 76
Medical Imaging on the Semantic Web: Annotation and Image Markup.
Rubin, D. L., Mongkolwat, P., Kleper, V., Supekar, K., & Channin, D. S. (2008) · Cited by 73
The Annotation and Image Mark-up Project
Channin, D. S., Mongkolwat, P., Kleper, V., & Rubin, D. L. (2009) · Radiology · doi:10.1148/radiol.2533090135 · Cited by 55
Critical role of spatial resolution in dynamic contrast‐enhanced breast MRI
Furman‐Haran, E., Grobgeld, D., Kelcz, F., & Degani, H. (2001) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.1123 · Cited by 53
Modeling microbial community structure and functional diversity across time and space
Larsen, P. E., Gibbons, S. M., & Gilbert, J. A. (2012) · FEMS Microbiology Letters · doi:10.1111/j.1574-6968.2012.02588.x · Cited by 47
Thews, O., Nowak, M., Sauvant, C., & Gekle, M. (2011) · Advances in experimental medicine and biology · doi:10.1007/978-1-4419-7756-4_16 · Cited by 44
Famis: A software package for functional feature extraction from biomedical multidimensional images
Frouin, F., Bazin, J., Paola, M. D., Jolivet, O., & Paola, R. D. (1992) · Computerized Medical Imaging and Graphics · doi:10.1016/0895-6111(92)90121-o · Cited by 41
Kawata, Y., Niki, N., Ohmatsu, H., Kusumoto, M., Tsuchida, T., Eguchi, K., Kaneko, M., & Moriyama, N. (2012) · Medical Physics · doi:10.1118/1.3679017 · Cited by 35
Mapping LIDC, RadLex™, and Lung Nodule Image Features
Opulencia, P., Channin, D. S., Raicu, D. S., & Furst, J. D. (2010) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-010-9285-6 · Cited by 30
Patel, L. R., Nykter, M., Chen, K., & Zhang, W. (2012) · Cancer Letters · doi:10.1016/j.canlet.2012.10.018 · Cited by 28
Walker‐Samuel, S., Orton, M. R., Boult, J. K. R., & Robinson, S. P. (2010) · Magnetic Resonance in Medicine · doi:10.1002/mrm.22572 · Cited by 28
Informatics methods to enable sharing of quantitative imaging research data
Levy, M. A., Freymann, J., Kirby, J., Fedorov, A., Fennessy, F. M., Eschrich, S. A., Berglund, A., Fenstermacher, D. A., Tan, Y., Guo, X., et al. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.04.007 · Cited by 25
Canuto, H. C., McLachlan, C., Kettunen, M. I., Velić, M., Krishnan, A. S., Neves, A., de Backer, M., Hu, D., Hobson, M., & Brindle, K. M. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21946 · Cited by 21
MR imaging of the musculoskeletal soft tissue mass: is heterogeneity a sign of malignancy?
Pang, K. K., & Hughes, T. H. (2003) · PubMed · Cited by 18
Factor analysis of medical image sequences in MR of head and neck tumors.
Zagdanski, A., Sigal, R., Bosq, J., Bazin, J., Vanel, D., & Paola, R. D. (1994) · PubMed Central · Cited by 17
RECIST: right time to renovate?
Burton, A. (2007) · The Lancet Oncology · doi:10.1016/s1470-2045(07)70155-9 · Cited by 16
PET Imaging of the Impact of Extracellular pH and MAP Kinases on the p-Glycoprotein (Pgp) Activity
Thews, O., Dillenburg, W., Rösch, F., & Fellner, M. (2012) · Advances in experimental medicine and biology · doi:10.1007/978-1-4614-4989-8_39 · Cited by 10
Flagged first, then by citations
1 of the 51 references couldn’t be matched to a known paper.
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
Radiomics: Images Are More than Pictures, They Are Data
Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016) · Radiology · doi:10.1148/radiol.2015151169 · Cited by 8,421
Self-citationRadiomics: the bridge between medical imaging and personalized medicine
Lambin, P., Leijenaar, R. T. H., Deist, T. M., Peerlings, J., de Jong, E. E. C., van Timmeren, J., Sanduleanu, S., Larue, R. T. H. M., Even, A. J. G., Jochems, A., et al. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.141 · Cited by 5,688
Artificial intelligence in cancer imaging: Clinical challenges and applications
Bi, W. L., Hosny, A., Schabath, M. B., Giger, M. L., Birkbak, N. J., Mehrtash, A., Allison, T., Arnaout, O., Abbosh, C., Dunn, I. F., et al. (2019) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21552 · Cited by 1,846
Self-citationLiu, Z., Wang, S., Dong, D., Wei, J., Fang, C., Zhou, X., Sun, K., Li, L., Li, B., Wang, M., et al. (2019) · Theranostics · doi:10.7150/thno.30309 · Cited by 983
Tumour evolution in hepatocellular carcinoma
Craig, A. J., von Felden, J., García‐Lezana, T., Sarcognato, S., & Villanueva, A. (2019) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-019-0229-4 · Cited by 842
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
