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.
Many prediction models that consider factors related to disease and treatment are available, but lack standardized assessments of their robustness, reproducibility or clinical utility
The complete cycle of model development for decision making in radiotherapy involves several stages, including selection of data, performance measure, classification and external validation
Clinical decision-support systems (CDSSs) based on validated predictors will be crucial to implement personalized radiation oncology
Tolerance of normal tissue is the dose-limiting factor for the administration of radiotherapy, therefore, any CDSS should be based on predictors of tumour control and the probability of complications
Rapid-learning healthcare will enable the increasingly rapid validation of CDSSs, which, in turn, will enable the next major advances in shared decision making
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We acknowledge financial support from the Center for Translational Molecular Medicine framework (AIR FORCE), European Union sixth and seventh framework programme (ARTFORCE and METOXIA), INTERREG (www.eurocat.info), QuIC-ConCePT (funded by the Innovative Medicine Initiative Joint Undertaking) and the Dutch Cancer Society (KWF UM 2011-5020 and KWF UM 2009-4454).
The authors declare no competing financial interests.
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Lambin, P., van Stiphout, R., Starmans, M. et al. Predicting outcomes in radiation oncology—multifactorial decision support systems. Nat Rev Clin Oncol 10, 27–40 (2013). https://doi.org/10.1038/nrclinonc.2012.196
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