Authors
Mohamed Sobhi Jabal, Jikai Zhang, Dominic LaBella, Jessica L Houk, Dylan Zhang, Jeffrey D Rudie, Maciej A Mazurowski, Kirti Magudia, Evan Calabrese
Published in
Radiology. Artificial intelligence. Pages e260234. Oct 21, 2026. Epub Oct 21, 2026.
Abstract
The Brain Tumor Reporting and Data System (BT-RADS) standardizes posttreatment MRI response assessment in patients with diffuse gliomas but requires complex integration of imaging trends, medication effects, and radiation timing. An end-to-end multiagent large language model and convolutional neural network system for automated BTRADS classification was developed and evaluated. Five hundred nine consecutive posttreatment glioma MRI examinations (March 2017-July 2021) from a single center were retrospectively evaluated. An extractor agent identified clinical variables from notes using gpt-oss (OpenAI, 20-billion parameters). A scorer agent applied BTRADS decision logic integrating these with quantitative volumetrics. System accuracy was compared against initial assessments, using expert annotation as the reference standard. Four hundred ninety-two examinations were evaluable. Overall system accuracy was 76.0% (374/492) versus 57.5% (283/492) for initial assessments, an absolute difference of +18.5 percentage points (P < .001). Cohen kappa between the system and the reference standard was 0.71. The system achieved high sensitivity in context-dependent categories (BT-1b; 100%; BT-1a, 92.7%; BT-3a, 87.5%) and moderate sensitivity for threshold-dependent categories (BT-3c, 74.8%; BT-2, 69.2%; BT-4, 69.3%; BT-3b, 57.1%). The BT-4 positive predictive value was 92.9%. These results support further evaluation of this system as a clinical decision-support tool, with local open-weight inference preserving data privacy.
PMID:
42905016
Bibliographic data and abstract were imported from PubMed on 23 Oct 2026.
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