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.

Electronic portal imaging devices (EPIDs) are not only applied for patient setup verification and detection of organ motion but are also increasingly used for dosimetric verification. The aim of our work is to obtain accurate dose distributions from a commercially available amorphous silicon (a-Si) EPID for transit dosimetry applications. For that purpose, a global calibration model was developed, which includes a correction procedure for ghosting effects, field size dependence and energy dependence of the a-Si EPID response. In addition, the long-term stability and additional buildup material for this type of EPID were determined. Differences in EPID response due to photon energy spectrum changes have been measured for different absorber thicknesses and field sizes, yielding off-axis spectrum correction factors based on transmission measurements. Dose measurements performed with an ionization chamber in a water tank were used as reference data, and the accuracy of the dosimetric calibration model was determined for a large range of treatment conditions. Gamma values using 3% as dose-difference criterion and 3 mm as distance-to-agreement criterion were used for evaluation. The field size dependence of the response could be corrected by a single kernel, fulfilling the gamma evaluation criteria in case of virtual wedges and intensity modulated radiation therapy fields. Differences in energy spectrum response amounted up to 30%-40%, but could be reduced to less than 3% using our correction model. For different treatment fields and (in)homogeneous phantoms, transit dose distributions satisfied in almost all situations the gamma criteria. We have shown that a-Si EPIDs can be accurately calibrated for transit dosimetry purposes.

Truly personalised cancer treatment is the goal in modern radiotherapy. However, personalised cancer treatment is also an immense challenge. The vast variety of both cancer patients and treatment options makes it extremely difficult to determine which decisions are optimal for the individual patient. Nevertheless, rapid learning health care and cohort multiple randomised controlled trial design are two approaches (among others) that can help meet this challenge.