2026-04-17T03:48:38Zhttps://recercat.cat/oai/request

oai:recercat.cat:11351/90762024-06-06T08:37:49Zcom_2072_378070com_2072_378040col_2072_378092

00925njm 22002777a 4500 dc Hernandez Sanchez, Alberto author González-Martínez, Teresa author Martínez-Elicegui, Javier author Pérez-Encinas, Manuel author Fox, Maria Laura author Mosquera Orgueira, Adrián author Arellano-Rodrigo, Eduardo author 2023-03-01T11:20:52Z 2023-03-01T11:20:52Z 2023-01 Aprendizaje automático; Mielofibrosis Aprenentatge automàtic; Mielofibrosi Machine learning; Myelofibrosis 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. Intel·ligència artificial - Aplicacions a la medicina Mielofibrosi Sang - Malalties INFORMATION SCIENCE::Information Science::Computing Methodologies::Algorithms::Artificial Intelligence::Machine Learning DISEASES::Hemic and Lymphatic Diseases::Hematologic Diseases::Bone Marrow Diseases::Myeloproliferative Disorders DISEASES::Neoplasms::Neoplasms by Histologic Type::Leukemia::Leukemia, Myeloid::Leukemia, Myeloid, Acute CIENCIA DE LA INFORMACIÓN::Ciencias de la información::metodologías computacionales::algoritmos::inteligencia artificial::aprendizaje automático ENFERMEDADES::enfermedades hematológicas y linfáticas::enfermedades hematológicas::enfermedades de la médula ósea::trastornos mieloproliferativos ENFERMEDADES::neoplasias::neoplasias por tipo histológico::leucemia::leucemia mieloide::leucemia mieloide aguda Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis