Predicting outcomes in radiation oncology-multifactorial decision support systems

Philippe Lambin*, R. van Stiphout, Maud H. W. Starmans, Emmanuel Rios-Velazquez, Georgi Nalbantov, Hugo J. W. L. Aerts, Erik Roelofs, Wouter van Elmpt, Paul C. Boutros, Pierluigi Granone, Vincenzo Valentini, Adrian C. Begg, Dirk De Ruysscher, Andre Dekker

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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. Lambin, P. et al. Nat. Rev. Clin. Oncol. 10, 27-40 (2013); published online 20 November 2012; doi:10.1038/nrclinonc.2012.196
Original languageEnglish
Pages (from-to)27-40
JournalNature Reviews Clinical Oncology
Issue number1
Publication statusPublished - Jan 2013


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