Real-time multimodal AI outperforms conventional scores for early risk prediction in BCMA CAR-T–Treated myeloma

Abstract Background: B-Cell Maturation Antigen (BCMA)-directed CAR-T therapy has transformed the treatment paradigm for patients with relapsed/refractory multiple myeloma (RRMM), yet 30-50% of patients progress within 12 months, and PET-positive extramedullary disease emerges in one-third, limiting durable disease control. In parallel, an expanding array of bispecific antibodies, next-generation CAR constructs, and trials offer actionable alternatives for patients predicted to fail standard BCMA-CAR-T. Contemporary prognostic scores derived from limited clinical variables offer modest discrimination and rarely inform risk-adapted care. We therefore investigated whether an explainable multimodal artificial-intelligence (MAI) framework that integrates clinical, serologic, cytogenetic, and quantitative imaging could sharpen early risk prediction after BCMA-CAR-T. Methods: Twenty-seven baseline variables were captured, including pre-lymphodepletion (pre-LD) circulating serum soluble BCMA (sBCMA; R&D Systems, Minneapolis, MN; catalog no. DY193), ferritin, C-reactive protein, β2-microglobulin, absolute lymphocyte count (ALC), ISS stage, plasma cell high-risk fluorescence-in-situ hybridization (del17p, t(4;14), t(14;16), chromosome 1 abnormalities), and metabolic tumor volume (MTV) extracted from pre-LD ¹⁸F-FDG PET/CT scans as previously described (Freeman Blood 2024). Patients with complete data formed the modelling cohort. Explainable machine learning algorithms based on Elastic Net, Random Survival Forest (RSF), and Gradient-Boosting Survival Machine (GBSM) models were trained with 5-fold cross-validation with multiple randomized initializations to mitigate overfitting bias. Harrell’s concordance index (C-index) quantified prognostic accuracy for progression-free (PFS) and overall survival (OS). Performance was benchmarked against existing risk models: MyCARe (Gagelmann, JCO, 2024), Stratification of CAR-T Outcomes at Pre-Apheresis Evaluation (SCOPE), and established tumor burden measurements (TMB) based on soluble BCMA (sBCMA) and PET-derived pre-treatment MTV (Freeman, Blood, 2024). Predictor importance was interrogated with permutation analysis and SHAP values. The Nelson-Aalen estimator was used for accumulated risk analysis and compared MAI-derived risk strata. Results: We retrospectively analyzed 183 consecutive RRMM patients infused with idecabtagene vicleucel or ciltacabtagene autoleucel between May 5th, 2021, and June 5th, 2024. Median duration of follow-up of all living patients was 22.1 months (range 2.8-44.1), and baseline patient demographics have been previously published and aligned with real-world expectations (Freeman Blood 2024). The Elastic net achieved c-index of 0.625 ± 0.125 and 0.635 ± 0.170 for PFS and OS, respectively, while GBSM yielded c-index of 0.690 ± 0.089 and 0.641 ± 0.179, respectively. A fine-tuned RSF slightly outperformed other MAI models, delivered c-indices of 0.701 ± 0.073 (PFS) and 0.674 ± 0.192 (OS). Collectively, these MAI models outperformed existing conventional scores of MyCARe (0.611/0.627), SCOPE (0.612/0.633), and tumor-burden (TMB,0.629/0.467). For instance, the RSF MAI model stratified patients into low-, intermediate-, and high-risk groups with 12-month progression risks of 12.8 %, 47.9 %, and 85.0 %, corresponding to PFS rates of 87.2 %, 52.1 %, and 15.0 %, respectively (log-rank p < 0.001). The most influential features for PFS in the RSF MAI model included pre-LD sBCMA, pre-LD ferritin, and ALC at apheresis, with MTV also contributing to the performance of the overall model. Dominating OS features were pre-LD sBCMA, pre-LD albumin and LDH. GBSM identified overlapping features with the addition of β2-microglobulin for PFS and CRP for OS. Conclusion: This explainable multimodal-AI platform already outperforms available clinically derived prognostic scores by unifying tumor-burden, inflammatory, biomarker and serological signals; ongoing expansion is already underway to incorporate whole-genome sequencing, digital pathology, and longitudinally collected data which is expected to yield an even more powerful, continuously learning risk-engine that can guide patient management, adaptive trial design, inform pre-emptive intervention strategies, and ultimately individualize management across a highly diverse myeloma population. These findings support further integration of multimodal AI for precision risk stratification in RRMM and warrant prospective validation in larger cohorts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.