2026-09-08T00:28:54Zhttps://gredos.usal.es/oai/request
oai:gredos.usal.es:10366/1720102026-07-02T00:00:56Zcom_10366_132632com_10366_4512com_10366_3823col_10366_132633
Gestión del Repositorio Documental de la Universidad de Salamanca author Mosquera Orgueira, Adrián author Pérez Encinas, Manuel author Hernández Sánchez, Alberto author González Martínez, Teresa author Arellano Rodrigo, Eduardo author Martínez Elicegui, Javier author Villaverde Ramiro, Ángela author Raya, José María author Ayala, Rosa author Ferrer-Marín, Francisca author Fox, María Laura author Vélez, Patricia author Mora, Elvira author Xicoy, Blanca author Mata Vázquez, María Isabel author García-Fortes, María author Angona, Anna author Cuevas, Beatriz author Senín, María Alicia author Ramírez Payer, Ángel author Ramírez, María José author Pérez López, Raúl author González de Villambrosía, Sonia author Martínez Valverde, Clara author Gómez Casares, María Teresa author García Hernández, Carmen author Gasior, Mercedes author Bellosillo, Beatriz author Steegmann, Juan Luis author Álvarez Larrán, Alberto author Hernández Rivas, Jesús María author Hernández Boluda, Juan Carlos 2026-07-01T06:52:44Z 2026-07-01T06:52:44Z 2023-01 Mosquera-Orgueira, A., Pérez-Encinas, M., Hernández-Sánchez, A., González-Martínez, T., Arellano-Rodrigo, E., Martínez-Elicegui, J., Villaverde-Ramiro, Á., Raya, J.-M., Ayala, R., Ferrer-Marín, F., Fox, M.-L., Velez, P., Mora, E., Xicoy, B., Mata-Vázquez, M.-I., García-Fortes, M., Angona, A., Cuevas, B., Senín, M.-A., … on behalf of the Spanish MPN Group (GEMFIN). (2023). Machine learning improves risk stratification in myelofibrosis: An analysis of the spanish registry of myelofibrosis. HemaSphere, 7(1), e818. https://doi.org/10.1097/HS9.0000000000000818 http://hdl.handle.net/10366/172010 10.1097/HS9.0000000000000818 36570691 2572-9241 [EN]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. eng Attribution-NonCommercial-NoDerivatives 4.0 International Myelofibrosis Machine learning improves risk stratification in myelofibrosis: An analysis of the Spanish registry of myelofibrosis info:eu-repo/semantics/article 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 URL https://gredos.usal.es/bitstream/10366/172010/1/23GMO%204_Adri%c3%a1nMO_machinelearningriskstratificationmyelofibrosis.pdf File MD5 9ea3fb6e1ba9398d487a7bd3bf777ab4 3649509 application/pdf 23GMO 4_AdriánMO_machinelearningriskstratificationmyelofibrosis.pdf URL https://gredos.usal.es/bitstream/10366/172010/4/23GMO%204_Adri%c3%a1nMO_machinelearningriskstratificationmyelofibrosis.pdf.txt File MD5 bf4988034e4295dfa7b5142b8297e5b6 44428 text/plain 23GMO 4_AdriánMO_machinelearningriskstratificationmyelofibrosis.pdf.txt