NIMG-81. PREDICTING TUMOR PROGRESSION WITH FIBER DENSITY-WEIGHTED WHITE MATTER PATHLENGTH MAPS IN PATIENTS WITH GLIOBLASTOMA
Bo Liu, Tiffany Ngan, Angela Jakary, Nate Tran, Paul Rowley, Steve Braunstein, Olivier Morin, Hui Lin, Janine LupoAbstract
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.