Predicting outcomes in radiation oncology--multifactorial decision support systems.
Predicting outcomes in radiation oncology--multifactorial decision support systems.
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DOI：
10.1038/nrclinonc.2012.196
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发表时间：
2013-01
期刊：
影响因子：
--
通讯作者：
Dekker A
中科院分区：
文献类型：
--
作者：
Lambin P;van Stiphout RG;Starmans MH;Rios-Velazquez E;Nalbantov G;Aerts HJ;Roelofs E;van Elmpt W;Boutros PC;Granone P;Valentini V;Begg AC;De Ruysscher D;Dekker A
随着个体化医疗的出现以及可用医疗数据量的增加和复杂性的提高，基于治疗结果预测模型开发临床决策支持系统的需求日益增长。在放射肿瘤学中，这些模型结合了来自临床、影像、分子和其他来源的预测性和预后性数据因素，以实现最高的准确性来预测肿瘤反应和随访事件发生率。在本综述中，我们概述了放射肿瘤学中与结果（包括生存、复发模式和毒性）相关的因素，并讨论了预测模型开发背后的方法，这是一个多阶段的过程。即使在初步开发和临床引入之后，一个真正有用的预测模型也将在来自不同地区的不同患者数据集上不断重新评估，以确保其针对特定人群的有效性。未来，经过验证的决策支持系统将完全整合到临床中，数据和知识将以标准化、即时和全球的方式共享。
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.