Optimal Stopping in Radiotherapy – Adaptive Control of IMRT

Progression-Based Adaptation of Radiotherapy Plans

Together with Massachusetts General Brigham – a teaching affiliate of Harvard Medical School in Boston – we are researching new models and methods for the individualized control of IMRT treatments. The aim is to be able to flexibly adapt ongoing radiation therapies based on the actual course of the disease. The models should recognize when a change of plan makes sense and provide the treating radiation oncologists with concrete suggestions for adjustments. In this way, the chances of recovery from IMRT can be exploited even more effectively.

AI Helps With Decision-Making – Modeling Based on Real Therapy Processes

The starting point for our research is retrospective data from IMRT treatments that have already been carried out and observed courses of treatment. We use Artificial Intelligence (AI) methods and mathematical statistics to create models that evaluate the course of treatment to date and predict the expected treatment progress and possible side effects.

These models are also used to determine options for adapting the current IMRT plan. On this basis, various options for adapting the current therapy can be identified. With the help of multi-criteria decision support, physicians can systematically compare these options – and thus make an informed decision as to which approach is best for their patients.

Project Partners

Massachusetts General Brigham and Harvard Medical School, Boston (USA)