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

Background: This multicentre retrospective observational 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).

Results: Sixty-five patients of R/R B-cell lymphoma treated with CAR-T cells in two centres were included. Pre-infusion [¹⁸F]FDG PET/CT scans and clinical data were systematically collected, and imaging features, including kurtosis, entropy, maximum diameter, standardized uptake value (SUV) related features (SUV_max, SUV_mean, SUV_std, SUV_median, SUV_p25, SUV_p75), total metabolic tumour volume (MTV_total), and total lesion glycolysis (TLG_total), were extracted using the Quibim platform. The median age was 62 (range 21-76) years and 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 Cox proportional-hazards models for CAR-T response at 3 and 6 months demonstrated high accuracy (AUC: 0.754 and 0.818, respectively). Median predicted OS after CAR-T therapy was 4.73 months for high MTV_total and 37.55 months for low MTV_total. Median 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.