AI-enabled multimodal adjudication of revised IMWG progression after BCMA CAR-T.

7521 Background: Real-world ascertainment of clinically meaningful relapse after BCMA CAR-T is challenging because progression signals are distributed across laboratories, imaging, pathology, and clinician actions and are frequently embedded in unstructured external reports. The revised IMWG criteria (Kumar et al., IMS 2025) provide standardized definitions for imaging-based progression (PET/CT and WB-MRI) enabling automated recognition of radiologic and serologic progression thresholds. We developed an AI-enabled multimodal framework to automate real-time derivation of recognized IMWG progression across fragmented care settings. Methods: We analyzed 183 BCMA CAR-T treatment episodes with longitudinal routine laboratory data and independent dual-reviewer adjudication of progression dates. Automated detection evaluated every M-protein and FLC measurement longitudinally in a continuous IMWG rules engine, in addition to flagging new hypercalcemia. In addition, initiation of a new line of therapy (considered a progression event), together with large-language-model extraction of radiologic progression from PET/CT, WB-MRI, CT, X-ray, and MRI brain/spine reports. Performance was assessed using accuracy and specificity. Results: Serologic progression from real-world lab feeds was detected with high reproducibility, with FLC and M-protein achieving high accuracy (96.5% and 98.5%, respectively) and specificity (>97%), indicating minimal premature triggering across serial measurements. Hypercalcemia was rare but highly specific for progression. Radiology-based AI extraction achieved high accuracy (91%) and specificity (92.1%), enabling reliable identification of imaging-defined relapse. Initiation of a new line of therapy occasionally occurred before formal serologic IMWG thresholds were met, reflecting clinician-recognized relapse or that driven by non-serologic disease. Notably, 19% of adjudicated relapses were triggered by radiologic or marrow criteria when serologic IMWG thresholds were absent or lagging, including 12.5% radiology-only and 5.8% marrow-only events, aligned with our published data for post CART relapses (Abuhelwa et al Front. Oncol 2025). Conclusions: Automated lab-only approaches systematically underestimate true IMWG progression after BCMA CAR-T. An AI-enabled multimodal adjudication framework aligned with the revised IMWG criteria enables scalable, real-time, and reproducible progression capture for clinical trials and real-world datasets, supporting rapid endpoint determination, regulatory-grade retrospective analyses, and biologically faithful reconstruction of relapse patterns after CAR-T.