Towards a clinically practical computational platform for systematically adapting radiation therapy for glioma...

Objective.High-grade gliomas (HGG) are aggressive brain tumors with poor prognoses despite an intense standard-of-care (SOC) chemoradiation protocol. Radiotherapy (RT) guidelines use static dose maps, which may not fully account for intratumoral heterogeneities and areas of tumor progression over the course of treatment. We address this limitation by guiding radiation delivery using imaging data to identify proliferative areas which are then targeted by a dose boost, and use mathematical modeling to simulate treatment response between the adjusted and the SOC treatment plan.Approach.Quantitative magnetic resonance imaging data for 15 HGG patients were collected at baseline and the third week of treatment to identify regions of high proliferation to guide the adapted radiation dose plan. The resulting dose plan is then adjusted by a board-certified medical physicist to ensure deliverability using clinically available linear accelerators. The plans are then used to virtually 'treat' each patient via our biology-based mathematical model, individually calibrated to each patient's imaging data acquired prior to dose adaptation.Main results.Our adaptation pipeline predicted a median (range) decrease in tumor burden of 31% (12% and 57%) one month post-therapy across the patient cohort compared to the SOC predicted tumor burden (p< 0.05). After adjusting the dose plan for clinical deliverability, it still predicted a decrease in tumor burden of 30% (7% and 62%) which is also significantly different than the SOC protocol (p< 0.05).Significance.Our clinical-computational platform, integrating mathematical modeling, dose painting, and adaptive RT, can identify alternative radiation dose plans hypothesized to yield statistically greater tumor control than the SOC prescriptions.