Keywords: Tumors (Post-Treatment), Cancer, DTI, AI, Progression, GBM
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.
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