Optimal Stopping in Radiotherapy (OSRT)

• Applicant: Privatdozent Dr. Christian Thieke
• Subject Area: Medical Physics, Biomedical Technology
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 448626611

Radiotherapy (RT) is a key modality against cancer that approx. half of the patients receive at some point during the course of their disease. It is typically delivered in a predefined number of fractions over several weeks, and the RT plan is modified only to account for variations in positioning or changes in anatomy. However, the response to the radiation in terms of tumor control and side effects can be individual for each patient, so it might be beneficial to optimize the treatment schedule based on an early assessment of the personal radiation response, e.g. by molecular biology, imaging, genetics, and also patient-reported outcome (PRO). In this setting the planning problem will not be regarded as a static, but as a dynamic sequential decision making problem, with the option of stopping the treatment at any given fraction during the therapy course. We term this ‘‘Optimal Stopping in radiotherapy (OSRT)”, after a similar concept in the fields of dynamic programming and Markov decision processes. We have assembled a network of researchers from radiation oncology, medical physics and mathematics to work on this topic. As a starting point, three specific subprojects have been defined: Dose modification strategies derived from FDG-PET in lung cancer radiotherapy, radiosensitivity estimation based on FLT-PET in canine nasal tumor radiotherapy, and a multi-biomarker Bayesian learning model for outcome prediction of stereotactic radiotherapy of liver tumors. Over the runtime of three years, the network members and invited guest experts will hold five workshops to discuss general developments in the field of treatment monitoring and predictive biomarkers and their possible impact on OSRT, mathematical optimization methods, exchange of data and models in a multi-institutional setting, involvement of physicians in the modeling process, progress on the specific subprojects, and identify possible additional subprojects. The planned result of the network activity is a concept for OSRT which provides mathematical tools and models for OSRT, can serve as a guideline for future clinical trials, and can ultimately contribute to making radiotherapy for cancer patients safer and more effective.

• DFG Programme: Scientific Networks
• Co-Investigator: Professor Dr. Nils Henrik Nicolay