Forecasting Patient-Specific Tumor Burden in Low-Grade Glioma Using Data Assimilation

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 & without gadolinium-based contrast, T2-fluid attenuated inversion recovery). MRI was acquired before treatment and approximately at the 4, 5, 6, and 12-month post-treatment visits. Brain and tumor regions were segmented using a semi-automated algorithm. Our biophysical model is a reaction-diffusion equation that explicitly accounts for tumor cell proliferation, invasion, and treatment response. The model focuses on changes in non-enhancing tumor regions, a hallmark of LGG. Total tumor cell count (TTC) derived from spatiotemporal tumor response forecasts was used to quantify tumor burden. The data assimilation framework incorporates MRI data acquired with each subsequent visit then updates its forecast. Predictive accuracy was quantified via the concordance correlation coefficient (CCC) between the observed and predicted TTC for both short (e.g., 1 - 3 months) and longer-term predictions (e.g., 6, 12-months). A Mann-Whitney U test compared short and longer-term prediction performance. The median and interquartile range (IQR) of the model parameters describing tumor cell proliferation and invasion are reported for each follow up visit. The tumors experienced a median volumetric change of -36.4% over 12 months. The model accurately forecasts TTCs at short (CCC: 0.71) and longer interval times (CCC: 0.96) with no statistically significant difference (p-value: 0.34) in performance between groups. The model estimated tumor cell proliferation rate had a median and IQR of 0.10 (0.06) at the 4-month visit, 0.07 (0.04) for the 5-month visit, 0.04 (0.05) at the 6-month visit, and 0.04 (0.04) days-1 at the 12-month visit. Similarly, the model estimated tumor diffusion coefficient had a median and IQR of 0.15 (0.10), 0.16 (0.10), 0.18 (0.06), and 0.16 (0.09) mm2/days for the 4, 5, 6, and 12-month visit. Our preliminary findings demonstrate the model’s ability to predict patient-specific LGG behavior and offers a step towards the development of a personalized decision support tool for managing LGG care. Citation Format: Sophia Ty, Devika Shankar, Bikash Panthi, Mohamad El-Jammal, Victoria White, Holly Langshaw, Eleni Konstantinopoulou, Hannah Green, Ashi Jain Chakresh, Vishantan Kumar, Thomas E. Yankeelov, Caroline Chung, David A. Hormuth. Developing a data assimilation framework to forecast patient-specific tumor burden in low-grade glioma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6840.