NIMG-81. PREDICTING TUMOR PROGRESSION WITH FIBER DENSITY-WEIGHTED WHITE MATTER PATHLENGTH MAPS IN PATIENTS WITH GLIOBLASTOMA

Abstract PURPOSE Although histopathological evidence indicates that glioma cells preferentially migrate along large white-matter bundles, standard-of-care (SOC) radiation therapy (RT) planning remains an isotropic expansion of the anatomical lesion. We hypothesize that anisotropic expansion of RT target volumes along white-matter pathways using fiber density-weighted, white-matter pathlength maps (DW-WMPLMs) derived from diffusion tensor imaging (DTI) could improve the prediction of tumor cell migration beyond surgical margins by a deep learning model compared to SOC clinical target volumes (CTVs). METHODS DTI and anatomical MRI from 118 patients newly-diagnosed with glioblastoma were retrospectively analyzed with anatomical imaging from post-surgical resection and latest progression scan before intervention. Parallel-transport tractography seeded from the pre-surgery T2-lesion was used to estimate white-matter fiber propagation. Fibers that intersected with the resection cavity were weighted by their density to control the length of expansion and create a DW-WMPLM that was used together with T2-FLAIR and T1-post-contrast images as inputs to a novel 3D-Swin-UNetR network, trained to predict the recurrence region using 4-fold cross-validation and evaluated in a holdout test-set (95/23 CV/test). New weighted-overlap, coverage, and sparing indices were introduced to evaluate coverage of the progressed lesion and healthy brain excluded. RESULTS 52% of patients had progression outside the standard 2cm-isotropic CTV margin, motivating the need for anisotropic CTVs. Our novel DW-WMPLMs had 87±20% and 83±18% overlap with contrast-enhancing and T2-lesion progression, respectively. The deep-learning model trained with DW-WMPLMs and anatomical images achieved 19% and 56% higher Dice and weighted-overlap indices compared to the isotropic 2cm-CTV(p<0.02), with less normal brain included. CONCLUSION This study demonstrates the feasibility and benefit of incorporating a novel metric of white-matter track characterization into deep learning progression prediction models of glioblastoma for RT-planning. Current work is validating findings in a prospective cohort acquired immediately prior to RT and incorporating other imaging metrics.