Vormgeving cover & lay out proefschrift

Future perspectives Decision support systems As indicated in Chapters 2 and 3 of this thesis there is still a lot of work to be done before decision support systems for GBM can be implemented in clinical practice. However, it is expected that within five years these systems will be able to present treating physicians with an accurate estimation of tumor control, toxicity, quality of life and cost effectiveness of a treatment. Once these systems have been integrated into daily clinical practice, there is one more important step to be taken: provide patients with the same information as their treating physicians receive. As patients often value outcomes very differently than their treating physician(s), wellinformed shared decision making is an essential part of personalized medicine94. By empowering the patient to actively participate in the decision-making higher levels of satisfaction with their treatment will be achieved, eventually resulting in a better short and long-term QoL. Although a treating physician presents all the relevant risks and benefits of a treatment during a consultation interview, this may not take into account the patient’s personal values and risk-balancing preferences between treatment benefits and harms such as quality-of-life versus quantity-of-life. Eliciting patient’s values and preferences is a complex task, as values of another person are difficult to anticipate and can be influenced by clinician’s views95. This would involve a new way of medical counselling, as physicians have difficulty predicting the treatment decisional preferences of their patients. Furthermore, presentation of unbiased medical information should be tailored to the patient’s needs, for instance via interactive online decision aids and videos. These are currently not yet available for glioma patients, and should be developed in the coming years to improve the patient’s ability to make the best decision for their individual situation. Furthermore, indicators of decision quality measuring patient’s expectations, satisfaction, and regret should be investigated. However, this does present an additional challenge.

Accounting for patient’s preferences will affect the way in which randomized trials can be conducted, especially when the study is designed to compare a “one size fits all” treatment to an individualized treatment. In a classical trial design, where treatment arms with different “one size fits all” treatments are compared, the primary endpoint (OS, local control, etc.) is defined unequivocally. This is cannot be the case in trials with a treatment arm involving individualized treatment, as certain patients will for instance attach more importance to QoL related aspects than to tumor control.

This explicitly and irrevocably inserts the patient’s selection bias into the treatment selections process. The challenge will be to still build a reasonable level of evidence.

This is clearly a field where more research is necessary.

The radiomics study presented in Chapter 3 has several limitations such as a small number of patients, manual delineation of the tumor on MR images registered to CT images with inter- and intraobserver variability and a lack of standardization in imaging protocols between the three participating institutes. Some of these may be - 164 -