The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Predicting outcomes in radiation oncology—multifactorial decision support systems
Open accessAbstract
With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical decision-support systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome—including survival, recurrence patterns and toxicity—in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process. Even after initial development and clinical introduction, a truly useful predictive model will be continuously re-evaluated on different patient datasets from different regions to ensure its population-specific strength. In the future, validated decision-support systems will be fully integrated in the clinic, with data and knowledge being shared in a standardized, instant and global manner.
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Self-citationDecoding 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-citationMachine Learning methods for Quantitative Radiomic Biomarkers
Parmar, C., Grossmann, P., Bussink, J., Lambin, P., & Aerts, H. J. W. L. (2015) · Scientific Reports · doi:10.1038/srep13087 · Cited by 1,019
Self-citationCT-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-citationSalahuddin, Z., Woodruff, H. C., Chatterjee, A., & Lambin, P. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105111 · Cited by 508
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Metadata
- Authors
- Lambin, P., van Stiphout, R. G. P. M., Starmans, M. H. W., Rios-Velazquez, E., Nalbantov, G., Aerts, H. J. W. L., Roelofs, E., van Elmpt, W., Boutros, P. C., Granone, P., Valentini, V., Begg, A. C., De Ruysscher, D., & Dekker, A.
- Venue
- Nature Reviews Clinical Oncology
- Published
- 2012-11-20
- Volume
- 10
- Issue
- 1
- Pages
- 27-40
- ISSN
- 1759-4774 (ISSN-L)
- 1759-4782
- OpenAlex
- W2128625986
- Cited by
- 402
- References
- 17
- FWCI
- 4.00
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Lambin, P., van Stiphout, R. G. P. M., Starmans, M. H. W., Rios-Velazquez, E., Nalbantov, G., Aerts, H. J. W. L., Roelofs, E., van Elmpt, W., Boutros, P. C., Granone, P., Valentini, V., Begg, A. C., De Ruysscher, D., & Dekker, A. (2013). Predicting outcomes in radiation oncology—multifactorial decision support systems. Nature Reviews Clinical Oncology. https://doi.org/10.1038/nrclinonc.2012.196
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