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Title: | Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis |
Author: | Mosquera Orgueira, Adrián Pérez Encinas, Manuel Hernández Sánchez, Alberto González Martínez, Teresa Arellano Rodrigo, Eduardo Martínez Elicegui, Javier Villaverde Ramiro, Ángela Raya, José María Ayala, Rosa Ferrer Marín, Francisca Fox, María Laura Velez, Patricia Mora, Elvira Xicoy, Blanca Mata Vázquez, María Isabel García Fortes, María Angona, Anna Cuevas, Beatriz Senín, María Alicia Ramírez Payer, Ángel Ramírez, María José Pérez López, Raúl González de Villambrosía, Sonia Martínez Valverde, Clara Gómez Casares, María Teresa García Hernández, Carmen Gasior, Mercedes Bellosillo Paricio, Beatriz Steegmann, Juan Luis Álvarez Larrán, Alberto Hernández Rivas, Jesús María Hernández Boluda, Juan Carlos The Spanish MPN Group (GEMFIN). |
Keywords: | Mielofibrosi Aprenentatge automàtic Pronòstic mèdic Myelofibrosis Machine learning Prognosis |
Issue Date: | 20-Dec-2022 |
Publisher: | Ovid Technologies (Wolters Kluwer Health) |
Abstract: | Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) with heterogeneous clinical course. Allogeneic hematopoietic cell transplantation remains the only curative therapy, but its morbidity and mortality require careful candidate selection. Therefore, accurate disease risk prognostication is critical for treatment decision-making. We obtained registry data from patients diagnosed with MF in 60 Spanish institutions (N = 1386). These were randomly divided into a training set (80%) and a test set (20%). A machine learning (ML) technique (random forest) was used to model overall survival (OS) and leukemia-free survival (LFS) in the training set, and the results were validated in the test set. We derived the AIPSS-MF (Artificial Intelligence Prognostic Scoring System for Myelofibrosis) model, which was based on 8 clinical variables at diagnosis and achieved high accuracy in predicting OS (training set c-index, 0.750; test set c-index, 0.744) and LFS (training set c-index, 0.697; test set c-index, 0.703). No improvement was obtained with the inclusion of MPN driver mutations in the model. We were unable to adequately assess the potential benefit of including adverse cytogenetics or high-risk mutations due to the lack of these data in many patients. AIPSS-MF was superior to the IPSS regardless of MF subtype and age range and outperformed the MYSEC-PM in patients with secondary MF. In conclusion, we have developed a prediction model based exclusively on clinical variables that provides individualized prognostic estimates in patients with primary and secondary MF. The use of AIPSS-MF in combination with predictive models that incorporate genetic information may improve disease risk stratification. |
Note: | Reproducció del document publicat a: https://doi.org/10.1097/HS9.0000000000000818 |
It is part of: | HemaSphere, 2023, vol. 7, num. 1, p. e818 |
URI: | http://hdl.handle.net/2445/193126 |
Related resource: | https://doi.org/10.1097/HS9.0000000000000818 |
ISSN: | 2572-924 |
Appears in Collections: | Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer) Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL)) |
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