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Simple Summary The interest in using Machine-Learning (ML) techniques in clinical research is growing. We applied ML to build up a novel prognostic model from patients affected with Mantle Cell Lymphoma (MCL) enrolled in a phase III open-labeled, randomized clinical trial from the Fondazione Italiana Linfomi (FIL)-MCL0208. This is the first application of ML in a prospective clinical trial on MCL lymphoma. We applied a novel ML pipeline to a large cohort of patients for which several clinical variables have been collected at baseline, and assessed their prognostic value based on overall survival. We validated it on two independent data series provided by European MCL Network. Due to its flexibility, we believe that ML would be of tremendous help in the development of a novel MCL prognostic score aimed at re-defining risk stratification. Background: Multicenter clinical trials are producing growing amounts of clinical data. Machine Learning (ML) might facilitate the discovery of novel tools for prognostication and disease-stratification. Taking advantage of a systematic collection of multiple variables, we developed a model derived from data collected on 300 patients with mantle cell lymphoma (MCL) from the Fondazione Italiana Linfomi-MCL0208 phase III trial (NCT02354313). Methods: We developed a score with a clustering algorithm applied to clinical variables. The candidate score was correlated to overall survival (OS) and validated in two independent data series from the European MCL Network (NCT00209222, NCT00209209); Results: Three groups of patients were significantly discriminated: Low, Intermediate (Int), and High risk (High). Seven discriminants were identified by a feature reduction approach: albumin, Ki-67, lactate dehydrogenase, lymphocytes, platelets, bone marrow infiltration, and B-symptoms. Accordingly, patients in the Int and High groups had shorter OS rates than those in the Low and Int groups, respectively (Int -> Low, HR: 3.1, 95% CI: 1.0-9.6; High -> Int, HR: 2.3, 95% CI: 1.5-4.7). Based on the 7 markers, we defined the engineered MCL international prognostic index (eMIPI), which was validated and confirmed in two independent cohorts; Conclusions: We developed and validated a ML-based prognostic model for MCL. Even when currently limited to baseline predictors, our approach has high scalability potential.

Simple Summary Mantle Cell Lymphoma (MCL) is a lymphoproliferative disorder which represents less than 10% of all non-Hodgkin Lymphomas. The typical course of MCL is characterized by several relapses (“remitting-relapsing” course), and since its identification it has been considered an incurable disease. Allogeneic stem cell transplantation (allo-SCT) has represented in the past years the only treatment which could ensure prolonged remissions, at least in younger patients. In our paper, we critically revised the available data on the use of allo-SCT in MCL. The aim of our review is to identify the subgroups of patients who could best benefit from this therapeutic strategy, the optimal timing for transplantation and the best ways to bridge patients to allo-SCT, in an era in which many novel agents have been developed. Abstract MCL is an uncommon lymphoproliferative disorder that has been regarded as incurable since its identification as a distinct entity. Allogeneic transplantation for two decades has represented the only option capable of ensuring prolonged remissions and possibly cure. Despite its efficacy, its application has been limited by feasibility limitations and substantial toxicity, particularly in elderly patients. Nevertheless, the experience accumulated over time has been wide though often scattered among retrospective and small prospective studies. In this review, we aimed at critically revise and discuss available evidence on allogeneic transplantation in MCL, trying to put available evidence into the 2020 perspective, characterized by unprecedented development of novel promising therapeutic agents and regimens.