Predicting survival, neurotoxicity and response in B-Cell lymphoma patients treated with CAR-T therapy using an imaging features-based model

Abstract

Background: This study aims to develop imaging-based prognostic and predictive models for relapsed/refractory (R/R) B-cell lymphoma patients undergoing CAR-T therapy by integrating clinical data and imaging features. Specifically, our aim was to predict 3- and 6-month treatment response, overall survival (OS), progression-free survival (PFS), and the occurrence of the immune effector cell-associated neurotoxicity syndrome (ICANS). Methods: This multicentre retrospective observational study includes 65 patients of R/R B-cell lymphoma treated with CAR-T cells in two centres. Pre-infusion 18F-FDG PET/CT scans and clinical data were systematically collected. Imaging features were extracted using the Quibim platform. Logistic regression was employed for predicting ICANS and CAR-T response, while univariate and multivariate analyses Cox proportional-hazards models were performed for survival prediction. Models with best overall fit based on Akaike information criterion and with 10-fold repeated cross-validation were selected. Results: Sixty-five patients with a median age of 62 (range 21–76) years were evaluated. The median follow-up for survivors was 10.47 (range 0.20–45.80) months. A logistic regression model accurately predicted neurotoxicity (AUC: 0.830), and models for CAR-T response at 3 and 6 months demonstrated high accuracy (AUC: 0.928 and 0.940, respectively). Median OS after CAR-T therapy was 4.73 months for high total metabolic tumour volume (MTV_total) and 37.55 months for low MTV_total. Predicted PFS was 2.73 months for high MTV_total and 11.83 months for low MTV_total. For all outcomes, predictive models, combining imaging features and clinical variables, showed improved accuracy compared to models using only clinical variables or imaging features alone. Conclusion: This study successfully integrates imaging features and clinical variables to predict outcomes in R/R B-cell lymphoma patients undergoing CAR-T. Notably, the identified MTV_total cut-off effectively stratifies patients, as evidenced by significant differences in OS and PFS. Additionally, the predictive models for neurotoxicity and CAR-T response show promising accuracy. This comprehensive approach holds promise for risk stratification and personalized treatment strategies which may become a helpful tool for optimizing CAR-T outcomes in R/R lymphoma patients.