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Abstract #0426

MRI-based Radiomics as a Predictive Biomarker of Survival in High Grade Gliomas Treated with Chimeric Antigen Receptor T-Cell Therapy

Sohaib Naim1, Chi Wah Wong2, Eemon Tizpa1, Hannah Jade Young1, Kimberly Jane Bonjoc1, Seth Michael Hilliard1, Aleksandr Filippov 1, Saman Tabassum Khan1, Christine Brown3, Behnam Badie4, and Ammar Ahmed Chaudhry1
1Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States, 2Applied AI and Data Science, City of Hope National Medical Center, Duarte, CA, United States, 3Hematology & Hematopoietic Cell Transplantation and Immuno-Oncology, City of Hope National Medical Center, Duarte, CA, United States, 4Surgery, City of Hope National Medical Center, Duarte, CA, United States

High grade gliomas (HGG) is the most common malignant primary brain tumors in adults. In this study, 61 patients with recurrent HGGs underwent surgical resection and chimeric antigen receptor-T cell therapy. Volumetric segmentations of contrast-enhanced (CE) and non-enhanced tumors (NET) using T1-weighted CE MR images were used to identify shape- and texture-based features from these regions of interest. We evaluated radiomic characteristics of these HGGs to determine novel imaging biomarkers to predict treatment response. Exponentially-filtered textural radiomic features based on Neighboring Gray Tone Difference Matrix and Gray Level Co-occurrence Matrix derived from NET were the strongest predictors of overall survival.

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