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Glioblastoma is a highly invasive brain tumor, whose cells infiltratesurrounding normal brain tissue beyond the lesion outlines visible in thecurrent medical scans. These infiltrative cells are treated mainly byradiotherapy. Existing radiotherapy plans for brain tumors derive frompopulation studies and scarcely account for patient-specific conditions. Herewe provide a Bayesian machine learning framework for the rational design ofimproved, personalized radiotherapy plans using mathematical modeling andpatient multimodal medical scans. Our method, for the first time, integratescomplementary information from high resolution MRI scans and highly specificFET-PET metabolic maps to infer tumor cell density in glioblastoma patients.The Bayesian framework quantifies imaging and modeling uncertainties andpredicts patient-specific tumor cell density with confidence intervals. Theproposed methodology relies only on data acquired at a single time point andthus is applicable to standard clinical settings. An initial clinicalpopulation study shows that the radiotherapy plans generated from the inferredtumor cell infiltration maps spare more healthy tissue thereby reducingradiation toxicity while yielding comparable accuracy with standardradiotherapy protocols. Moreover, the inferred regions of high tumor celldensities coincide with the tumor radioresistant areas, providing guidance forpersonalized dose-escalation. The proposed integration of multimodal scans andmathematical modeling provides a robust, non-invasive tool to assistpersonalized radiotherapy design.

Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans and Bayesian Inference