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• W2128625986 abstract "The emergence of individualized medicine has spurred the need for the development of clinical decision-support systems (CDSSs) based on prediction models of treatment outcome. In radiation oncology, CDSSs combine clinical, imaging and molecular factors to achieve the highest accuracy to predict tumour response. Here, the authors provide an overview of these factors—including survival, recurrence patterns and toxicity—and discuss the methodology behind the multistage development of CDSSs. 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." @default.
• W2128625986 created "2016-06-24" @default.
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• W2128625986 date "2012-11-20" @default.
• W2128625986 modified "2023-10-05" @default.
• W2128625986 title "Predicting outcomes in radiation oncology—multifactorial decision support systems" @default.
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