Towards a clinically practical computational platform for systematically adapting radiation therapy for glioma
Hugo Joseph Michel Miniere1, David Hormuth2, Ernesto Augusto Bueno da Fonseca Lima2
1Biomedical Engineering, The University of Texas at Austin, 2515 Speedway, Austin, Texas, 78712-1139, United States.
Physics in Medicine and Biology
|August 6, 2026
Summary
This study introduces an adaptive radiotherapy approach for high-grade gliomas (HGG) that uses imaging and mathematical models to personalize radiation doses. The adaptive plan significantly reduced predicted tumor burden compared to standard care, offering a promising strategy for improved HGG treatment.
Area of Science:
- Neuro-oncology
- Radiation Oncology
- Medical Imaging
Background:
- High-grade gliomas (HGG) have poor prognoses despite standard chemoradiation.
- Current radiotherapy uses static dose maps, neglecting tumor heterogeneity and progression.
- Limitations in standard protocols necessitate innovative treatment strategies.
Purpose of the Study:
- To develop and evaluate an adaptive radiotherapy pipeline for HGG.
- To integrate quantitative MRI, dose painting, and mathematical modeling for personalized treatment.
- To compare the predicted treatment response of an adaptive plan versus standard-of-care (SOC).
Main Methods:
- Quantitative MRI data from 15 HGG patients were analyzed at baseline and week 3.
- Proliferative regions identified via MRI guided an adapted radiation dose plan.
- A biology-based mathematical model simulated treatment response for both adaptive and SOC plans.
Main Results:
- The adaptive pipeline predicted a median 31% decrease in tumor burden post-therapy, significantly better than SOC.
- Even after clinical deliverability adjustments, the adaptive plan predicted a 30% tumor burden reduction (p < 0.05).
- Both adaptive plan simulations showed statistically significant improvements over the SOC protocol.
Conclusions:
- An adaptive radiotherapy platform integrating mathematical modeling and dose painting shows potential for enhanced HGG tumor control.
- This clinical-computational approach can identify alternative radiation dose plans superior to SOC.
- Personalized, adaptive radiation strategies may improve outcomes for aggressive brain tumors.
