A Clinical Prognostic Model Based on Machine Learning from the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial

Gian Maria Zaccaria, Simone Ferrero, Eva Hoster, Roberto Passera, Andrea Evangelista, Elisa Genuardi, Daniela Drandi, Marco Ghislieri, Daniela Barbero, Ilaria Del Giudice, Monica Tani, Riccardo Moia, Stefano Volpetti, Maria Giuseppina Cabras, Nicola Di Renzo, Francesco Merli, Daniele Vallisa, Michele Spina, Anna Pascarella, Giancarlo LatteCaterina Patti, Alberto Fabbri, Attilio Guarini, Umberto Vitolo, Olivier Hermine, Hanneke C. Kluin-Nelemans, Sergio Cortelazzo, Martin Dreyling, Marco Ladetto

Risultato della ricerca: Contributo su rivistaArticolo in rivistapeer review

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.

Lingua originaleInglese
Numero di articolo188
RivistaCancers
Volume14
Numero di pubblicazione1
DOI
Stato di pubblicazionePubblicato - 1 gen 2022

Fingerprint

Entra nei temi di ricerca di 'A Clinical Prognostic Model Based on Machine Learning from the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial'. Insieme formano una fingerprint unica.

Cita questo