A Novel Machine-Learning Model to Predict Early Relapse in Mantle Cell Lymphoma (MCL)

Background MCL is currently an incurable disease. Although survival has greatly improved due to the advent of more effective treatments, early relapse or refractoriness after the first-line treatment (1L) is independently associated with a dismal outcome (Visco et al, BJH 2019). An early identification of these higher-risk patients would allow to optimize their management strategies and, possibly, to improve their outcome. The purpose of this study was to develop a novel prognostic model index that could predict, from the time of diagnosis, the risk of early relapse. Methods Patients diagnosed with MCL from 6 Spanish centers between January 2000 and December 2021 and treated with ≥1 lines were retrospectively analyzed (training population). An external cohort from an additional Spanish hospital was used to further validate the score (test cohort). Patients were classified as early-POD (E-POD: refractory to 1L or relapsing ≤24 m from diagnosis) or late-POD (L-POD: relapse after 1L beyond 24 m from diagnosis or absence of relapse at data cut-off). All patients had a follow-up (FU) ≥24 m from diagnosis to be evaluable for POD24, except those dying from MCL 65 years, no previous transplant, advanced stage, high-risk MIPI, Ki67≥30% and blastoid morphology), the LASSO method selected a total of 4: age at diagnosis, stage (I-II vs. III-IV), MIPI group (low, intermediate or high risk) and morphologic variant (classic vs. blastoid/pleomorphic). These variables, ad