Abstract
To develop and evaluate an automated, AI-based, volumetric brain tumor MRI response assessment algorithm on a large cohort of patients treated at a high-volume brain tumor center. We retrospectively analyzed data from 634 patients treated for glioblastoma at a single brain tumor center over a 5-year period (2017-2021). The mean age was 56 +/-13 years. 372/634 (59%) patients were male, and 262/634 (41%) patients were female. Study data consisted of 3,403 brain MRI exams and corresponding standardized, radiologist-based brain tumor response assessments (BT-RADS). An artificial intelligence (AI)-based brain tumor response assessment algorithm was developed using automated, volumetric tumor segmentation. AI-based response assessments were evaluated for agreement with radiologist-based response assessments and ability to stratify patients by overall survival. Metrics were computed to assess the agreement using BTRADS as the ground-truth, fixed-time point survival analysis was conducted to evaluate the survival stratification, and associated P-values were calculated. For all BT-RADS categories, AI-based response assessments showed moderate agreement with radiologists' response assessments (F1 = 0.587-0.755). Kaplan-Meier survival analysis revealed statistically worse overall fixed time point survival for patients assessed as image worsening equivalent to RANO progression by human alone compared to by AI alone (log-rank P=0.007). Cox proportional hazard model analysis showed a disadvantage to AI-based assessments for overall survival prediction (P=0.012). AI-based volumetric glioblastoma MRI response assessment following BT-RADS criteria yielded moderate agreement for replicating human response assessments and slightly worse stratification by overall survival. GBM＝ Glioblastoma; RANO＝ Response Assessment in Neuro-Oncology; BTRADS＝ Brain Tumor Reporting and Data System; NLP = Natural Language Processing.
Published Version
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