The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Applications and limitations of radiomics
Open accessAbstract
Radiomics is an emerging field in quantitative imaging that uses advanced imaging features to objectively and quantitatively describe tumour phenotypes. Radiomic features have recently drawn considerable interest due to its potential predictive power for treatment outcomes and cancer genetics, which may have important applications in personalized medicine. In this technical review, we describe applications and challenges of the radiomic field. We will review radiomic application areas and technical issues, as well as proper practices for the designs of radiomic studies.
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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
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-citationFDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0
Boellaard, R., Delgado-Bolton, R., Oyen, W. J. G., Giammarile, F., Tatsch, K., Eschner, W., Verzijlbergen, F. J., Barrington, S. F., Pike, L. C., Weber, W. A., et al. (2015) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-014-2961-x · Cited by 3,303
Dangers of Using "Optimal" Cutpoints in the Evaluation of Prognostic Factors
Altman, D. G., Lausen, B., Sauerbrei, W., & Schumacher, M. (1994) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/86.11.829 · Cited by 1,143
CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma
Coroller, T. P., Grossmann, P., Hou, Y., Rios Velazquez, E., Leijenaar, R. T. H., Hermann, G., Lambin, P., Haibe-Kains, B., Mak, R. H., & Aerts, H. J. W. L. (2015) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2015.02.015 · Cited by 695
Self-citationIdentification 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
Cited by
Computational Radiomics System to Decode the Radiographic Phenotype
(2017) · doi:10.1158/0008-5472.can-17-0339 · Cited by 6,593
Radiomics: 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
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-citationMayerhoefer, M. E., Materka, A., Langs, G., Häggström, I., Szczypiński, P., Gibbs, P., & Cook, G. (2020) · Journal of Nuclear Medicine · doi:10.2967/jnumed.118.222893 · Cited by 1,777
Radiomics in medical imaging—“how-to” guide and critical reflection
van Timmeren, J. E., Cester, D., Tanadini-Lang, S., Alkadhi, H., & Baessler, B. (2020) · Insights into Imaging · doi:10.1186/s13244-020-00887-2 · Cited by 1,316
Metadata
- Authors
- Yip, S. S. F., & Aerts, H. J. W. L.
- Venue
- Physics in Medicine and Biology
- Publisher
- IOP Publishing
- Published
- 2016-06-08
- Volume
- 61
- Issue
- 13
- Pages
- R150-R166
- ISSN
- 0031-9155 (ISSN-L)
- 1361-6560
- OpenAlex
- W2409649574
- Cited by
- 1,210
- References
- 7
- FWCI
- 62.81
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Yip, S. S. F., & Aerts, H. J. W. L. (2016). Applications and limitations of radiomics. Physics in Medicine and Biology. https://doi.org/10.1088/0031-9155/61/13/r150
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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