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The Biological Meaning of Radiomic Features
Open accessAbstract
Radiomic analysis offers a powerful tool for the extraction of clinically relevant information from radiologic imaging. Radiomics can be used to predict patient outcome through automated high-throughput feature extraction, using large training cohorts to elucidate subtle relationships between image characteristics and disease status. However powerful, the data-driven nature of radiomics inherently offers no insight into the biological underpinnings of the observed relationships. Early radiomics work was dominated by analysis of semantic, radiologist-defined features and carried qualitative real-world meaning. Following the rapid developments and popularity of machine learning approaches, the field moved quickly toward high-throughput agnostic analyses, resulting in increasingly large feature sets. This trend took the focus toward an increase in predictive power and further away from a biological understanding of the findings. Such a disconnect between predictor model and biological meaning will inherently limit broad clinical translation. Efforts to reintroduce biological meaning into radiomics are gaining traction in the field with distinct emerging approaches available, including genomic correlates, local microscopic pathologic image textures, and macroscopic histopathologic marker expression. These methods are presented in this review, and their significance is discussed. The authors predict that following the increasing pressure for robust radiomics, biological validation will become a standard practice in the field, thus further cementing the role of the method in clinical decision making. © RSNA, 2021 An earlier incorrect version appeared online. This article was corrected on February 10, 2021.
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Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
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: 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-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
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., et al. (2014) · Nature Communications · doi:10.1038/ncomms5006 · Cited by 5,132
Self-citationThe Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., et al. (2013) · Journal of Digital Imaging · doi:10.1007/s10278-013-9622-7 · Cited by 4,599
Zwanenburg, A., Vallières, M., Abdalah, M. A., Aerts, H. J. W. L., Andrearczyk, V., Apte, A., Ashrafinia, S., Bakas, S., Beukinga, R. J., Boellaard, R., et al. (2020) · Radiology · doi:10.1148/radiol.2020191145 · Cited by 4,177
Self-citationReview The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge
Tomczak, K., Czerwińska, P., & Wiznerowicz, M. (2015) · Współczesna Onkologia · doi:10.5114/wo.2014.47136 · Cited by 3,492
STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration
Cohen, J. F., Korevaar, D. A., Altman, D. G., Bruns, D. E., Gatsonis, C. A., Hooft, L., Irwig, L., Levine, D., Reitsma, J. B., de Vet, H. C. W., et al. (2016) · BMJ Open · doi:10.1136/bmjopen-2016-012799 · Cited by 2,570
Giaccone, G., Herbst, R. S., Manegold, C., Scagliotti, G., Rosell, R., Miller, V., Natale, R. B., Schiller, J. H., von Pawel, J., Pluzanska, A., et al. (2004) · Journal of Clinical Oncology · doi:10.1200/jco.2004.08.001 · Cited by 1,716
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Wu, X., Zhang, S., Zhang, Z., He, Z., Xu, Z., Wang, W., Jin, Z., You, J., Guo, Y., Zhang, L., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00670-2 · Cited by 43
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Metadata
- Authors
- Tomaszewski, M. R., & Gillies, R. J.
- Venue
- Radiology
- Published
- 2021-03
- Volume
- 298
- Issue
- 3
- Pages
- 505-516
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W4214920103
- Cited by
- 549
- References
- 98
- FWCI
- 43.24
- Type
- journal article
- Language
- en
- Open access
- green
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