Decision support systems for personalized and participative radiation oncology

Philippe Lambin, Jaap Zindler, Ben G.L. Vanneste, Lien Van De Voorde, Daniëlle Eekers, Inge Compter, Kranthi Marella Panth, Jurgen Peerlings, Ruben T.H.M. Larue, Timo M. Deist, Arthur Jochems, Tim Lustberg (+27 others)
2017 Advanced Drug Delivery Reviews  
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 fashionplease watch the animation: http://youtu.be/ ZDJFOxpwqEA) from all available/appropriate medical data (clinical,
more » ... atment, 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.
doi:10.1016/j.addr.2016.01.006 pmid:26774327 fatcat:rpfktbxw3rf4vewbiuj5alsave