Decision support systems for personalized and participative radiation oncology

作者：Lambin Philippe; Zindler Jaap; Vanneste Ben G L; Van De Voorde Lien; Eekers Danielle; Compter Inge; Panth Kranthi Marella; Peerlings Jurgen; Larue Ruben T H M; Deist Timo M; Jochems Arthur; Lustberg Tim; van Soest Johan; de Jong Evelyn E C; Even Aniek J G; Reymen Bart; Rekers Nicolle; van Gi**ergen Marike; Roelofs Erik; Carvalho Sara; Leijenaar Ralph T H; Zegers Catharina M L; Jacobs Maria; van Timmeren Janita; Brouwers Patricia; Lal Jonathan A

来源：Advanced Drug Delivery Reviews, 2017, 109: 131-153.

DOI：10.1016/j.addr.2016.01.006

• Radiotherapy
• Decision support systems
• Prediction models
• Shared decision making

A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multifaceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.

• 出版日期2017-1-15