RT Journal Article
SR Electronic
T1 Development and Evaluation of Automated Artificial Intelligence–Based Brain Tumor Response Assessment in Patients with Glioblastoma
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 990
OP 998
DO 10.3174/ajnr.A8580
VO 46
IS 5
A1 Zhang, Jikai
A1 LaBella, Dominic
A1 Zhang, Dylan
A1 Houk, Jessica L.
A1 Rudie, Jeffrey D.
A1 Zou, Haotian
A1 Warman, Pranav
A1 Mazurowski, Maciej A.
A1 Calabrese, Evan
YR 2025
UL http://www.ajnr.org/content/46/5/990.abstract
AB This project aimed 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 (AI-VTRA) algorithm was developed using automated, volumetric tumor segmentation. AI-VTRA results 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 BT-RADS 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-VTRA showed moderate agreement with radiologist 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 = .007). Cox proportional hazard model analysis showed a disadvantage to AI-based assessments for overall survival prediction (P = .012). In summary, our proposed AI-VTRA, following BT-RADS criteria, yielded moderate agreement for replicating human response assessments and slightly worse stratification by overall survival.2D2-dimensionalAIartificial intelligenceAI-VTRAartificial intelligence volumetric tumor response assessmentBT-RADSBrain Tumor Reporting and Data SystemC-indexconcordance indexFeTSFederated Tumor SegmentationGBMglioblastomaIDHisocitrate dehydrogenaseNLPnatural language processingOSoverall survivalRANOResponse Assessment in Neuro-OncologyRECISTResponse Evaluation Criteria in Solid TumorsSDstandard deviationVDETvolumetric differences for enhancing tumorVDFLAIRvolumetric differences for FLAIR