Forecasting Chemoradiation Response Midtreatment for High-Grade Gliomas Through Patient-Specific Biology-Based...

Area of Science:

• Neuro-oncology
• Radiotherapy
• Medical Imaging
• Computational Biology

Background:

• Current radiation therapy (RT) for high-grade glioma (HGG) relies on pre-treatment magnetic resonance imaging (MRI).
• Adaptive RT during treatment is possible but often based solely on anatomical tumor changes.
• There is a need for more precise methods to forecast tumor response during HGG treatment.

Purpose of the Study:

• To determine if a biology-based mathematical model, using patient-specific multiparametric MRI (mpMRI) data, can accurately predict HGG response during RT.
• To assess the model's ability to forecast tumor behavior at both volume and voxel levels.
• To enable more effective adaptive RT strategies for HGG.

Main Methods:

• Twenty-one HGG patients undergoing concurrent RT and chemotherapy were scanned weekly with mpMRI.
• Patient-specific mpMRI data from baseline to mid-treatment were used to personalize a family of mathematical models.
• The most parsimonious model predicted tumor response (cellularity, volume, extent) at volume and voxel levels during and post-RT.

Main Results:

• The model achieved high accuracy in predicting total tumor cellularity and volume up to 2 months post-RT (Pearson correlation coefficients >0.86).
• Excellent spatial overlap was observed between predicted and actual tumor extent during and after RT (Dice values >0.87 and >0.74, respectively).
• Voxel-level predictions showed high correlation coefficients (>0.90 during RT, >0.71 post-RT).

Conclusions:

• A biology-based computational framework, using patient-specific mpMRI data, accurately forecasts HGG spatiotemporal response during and after adaptive RT.
• This approach offers a powerful tool for personalizing HGG treatment.
• The findings support the integration of advanced imaging and modeling for adaptive radiotherapy.