Abstract Low-grade gliomas (LGG) typically grow gradually for years with limited clinical symptoms but may later exhibit erratic growth patterns and undergo transformation to high-grade glioma. This presents challenges to disease management creating a need for novel methods to simulate and predict LGG behavior. Towards this goal, we implemented a data assimilation framework utilizing a previously developed biophysical model capable of spatiotemporally forecasting patient-specific tumor dynamics. The study cohort includes nine LGG patients treated with radiation therapy at the MD Anderson Cancer Center. All patients were longitudinally monitored using magnetic resonance imaging (MRI) to assess tumor cellularity (diffusion-weighted MRI) and extent of disease (T1-weighted MRI with Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6840.
Ty et al. (Fri,) studied this question.