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.
Item Type: | Journal article |
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Faculties: | Medicine > Institute for Medical Information Processing, Biometry and Epidemiology |
Subjects: | 600 Technology > 610 Medicine and health |
URN: | urn:nbn:de:bvb:19-epub-84527-7 |
ISSN: | 2072-6694 |
Language: | English |
ID Code: | 84527 |
Deposited On: | 17. Jan 2022 12:04 |
Last Modified: | 17. Jan 2022 12:04 |
- BASE
- 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
- Google Scholar
- 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