TY - JOUR
T1 - Decision support systems for personalized and participative radiation oncology
AU - Lambin, Philippe
AU - Zindler, Jaap
AU - Vanneste, Ben G. L.
AU - Van De Voorde, Lien
AU - Eekers, Danielle
AU - Compter, Inge
AU - Panth, Kranthi Marella
AU - Peerlings, Jurgen
AU - Larue, Ruben T. H. M.
AU - Deist, Timo M.
AU - Jochems, Arthur
AU - Lustberg, Tim
AU - van Soest, Johan
AU - de Jong, Evelyn E. C.
AU - Even, Aniek J. G.
AU - Reymen, Bart
AU - Rekers, Nicolle
AU - van Gisbergen, Marike
AU - Roelofs, Erik
AU - Carvalho, Sara
AU - Leijenaar, Ralph T. H.
AU - Zegers, Catharina M. L.
AU - Jacobs, Maria
AU - van Timmeren, Janita
AU - Brouwers, Patricia
AU - Lal, Jonathan A.
AU - Dubois, Ludwig
AU - Yaromina, Ala
AU - Van Limbergen, Evert Jan
AU - Berbee, Maaike
AU - van Elmpt, Wouter
AU - Oberije, Cary
AU - Ramaekers, Bram
AU - Dekker, Andre
AU - Boersma, Liesbeth J.
AU - Hoebers, Frank
AU - Smits, Kim M.
AU - Berlanga, Adriana J.
AU - Walsh, Sean
PY - 2017/1/15
Y1 - 2017/1/15
N2 - 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. (C) 2016 Published by Elsevier B.V.
AB - 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. (C) 2016 Published by Elsevier B.V.
KW - Radiotherapy
KW - Decision support systems
KW - Prediction models
KW - Shared decision making
KW - CELL LUNG-CANCER
KW - GENOME-WIDE ASSOCIATION
KW - POSITRON-EMISSION-TOMOGRAPHY
KW - SINGLE NUCLEOTIDE POLYMORPHISMS
KW - DOUBLE-STRAND BREAKS
KW - INTENSITY-MODULATED RADIOTHERAPY
KW - COMPLICATION PROBABILITY-MODELS
KW - EPIDEMIOLOGY STROBE STATEMENT
KW - RANDOMIZED CLINICAL-TRIALS
KW - IMAGE-GUIDED RADIOTHERAPY
U2 - 10.1016/j.addr.2016.01.006
DO - 10.1016/j.addr.2016.01.006
M3 - (Systematic) Review article
C2 - 26774327
SN - 0169-409X
VL - 109
SP - 131
EP - 153
JO - Advanced Drug Delivery Reviews
JF - Advanced Drug Delivery Reviews
ER -