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, Johan van Soest, Evelyn E C de Jong, Aniek J G Even, Bart Reymen, Nicolle Rekers, Marike van Gisbergen, Erik Roelofs, Sara CarvalhoRalph T H Leijenaar, Catharina M L Zegers, Maria Jacobs, Janita van Timmeren, Patricia Brouwers, Jonathan A Lal, Ludwig Dubois, Ala Yaromina, Evert Jan Van Limbergen, Maaike Berbee, Wouter van Elmpt, Cary Oberije, Bram Ramaekers, Andre Dekker, Liesbeth J Boersma, Frank Hoebers, Kim M Smits, Adriana J Berlanga, Sean Walsh

Research output: Contribution to journalReview articleScientificpeer-review

Abstract

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 multi-faceted 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.

Original languageEnglish
Pages (from-to)131-153
Number of pages23
JournalAdvanced drug delivery reviews
Volume109
DOIs
Publication statusPublished - 15 Jan 2017

Keywords

  • Decision Support Systems, Clinical
  • Humans
  • Neoplasms/diagnosis
  • Precision Medicine/methods
  • Radiation Oncology/methods
  • Treatment Outcome

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