Pre-RT Fiber Density-Weighted White Matter Pathlength Maps Can Predict Tumor Progression in Patients with Glioblastoma Multiforme

Motivation: Utilizing the knowledge of glioma cells' infiltration along white matter pathways to better predict GBM progression. Goal(s): To enhance GBM progression prediction by analyzing the map of adjacent white matter fibers and building models to incorporate that map with anatomical MR. Approach: Developed a novel algorithm, DW-WMPL, from Diffusion-Tensor Imaging data that adjusts white matter fiber lengths to reveal possible tumor advancement. Employed deep learning models to predict progression with anatomical MRI and DW-WMPL maps. Results: DW-WMPL-enhanced deep learning models achieved higher precision in tumor delineation and reduced normal brain inclusion versus the standard 2cm radiation margin. Impact: The introduction of density-weighted white-matter path-length maps provides valuable insights into tumor cell migration, significantly refining GBM progression prediction. This advancement indicates a pivotal step towards personalized, more effective radiation therapy planning.