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Zaccaria, Gian Maria; Ferrero, Simone; Hoster, Eva; Passera, Roberto; Evangelista, Andrea; Genuardi, Elisa; Drandi, Daniela; Ghislieri, Marco; Barbero, Daniela; Del Giudice, Monica and Moia; Merli, Francesco; Vallisa, Daniele; Spina, Michele; Pascarella, Anna; Latte, Giancarlo; Patti, Caterina; Fabbri, Alberto; Guarini, Attilio; Vitolo, Umberto; Hermine, Olivier; Kluin-Nelemans, Hanneke C.; Cortelazzo, Sergio; Dreyling, Martin; Ladetto, Marco (2021): A Clinical Prognostic Model Based on Machine Learning from the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial. In: Cancers, Vol. 14, No. 1
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Abstract

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.