Individualizing Glioma Radiotherapy Planning by Optimization of Data and Physics-Informed Discrete Loss

Brain tumor growth is unique to each patient and extends beyond what is visible in imaging scans, infiltrating surrounding brain tissue. Understanding these hidden patient-specific progressions is essential for effective therapies. Current treatment plans for brain tumors, such as radiotherapy, typically involve delineating a uniform margin around the visible tumor on pre-treatment scans to target this invisible tumor growth. This "one size fits all" approach is derived from population studies and often fails to account for the nuances of individual patient conditions. We present the framework GliODIL which infers the full spatial distribution of tumor cell concentration from available multi-modal imaging. This is achieved through the newly introduced method of Optimizing the Discrete Loss (ODIL), where both data and physics-based constraints are softly assimilated into the solution. Our test dataset comprises 152 glioblastoma patients with pre-treatment imaging and post-treatment follow-ups for tumor recurrence monitoring. By blending data-driven techniques with physics-based constraints adapted for complex cases, GliODIL enhances recurrence prediction in radiotherapy planning, offering a superior alternative to traditional uniform margins and strict PDE adherence.