Machine Learning Improves Risk Stratification in Myelofibrosis : An Analysis of the Spanish Registry of Myelofibrosis
Mosquera Orgueira, Adrian (Hospital Clínico Universitario (Santiago de Compostela, Galícia))
Pérez-Encinas, Manuel (Hospital Clínico Universitario (Santiago de Compostela, Galícia))
Hernández-Sánchez, Alberto (Hospital Clínico Universitario (Salamanca))
González-Martínez, Teresa (Hospital Clínico Universitario (Salamanca))
Arellano-Rodrigo, Eduardo (Hospital Clínic i Provincial de Barcelona)
Martínez-Elicegui, Javier (Hospital Clínico Universitario (Salamanca))
Villaverde-Ramiro, Ángela (Hospital Clínico Universitario (Salamanca))
Raya, José-María (Hospital Universitario de Canarias (Tenerife, Espanya))
Ayala, Rosa (Hospital Universitario 12 de Octubre (Madrid))
Ferrer-Marín, Francisca (Hospital General Universitario Morales Meseguer (Múrcia))
Fox, Maria Laura (Vall d'Hebron Institut d'Oncologia)
Velez, Patricia (Hospital del Mar (Barcelona, Catalunya))
Mora, Elvira (Hospital Universitari i Politècnic La Fe (València))
Xicoy, Blanca (Institut Germans Trias i Pujol. Institut de Recerca contra la Leucèmia Josep Carreras)
Mata-Vázquez, María-Isabel (Hospital Costa del Sol (Marbella, Espanya))
García Fortes, María (Hospital Universitario Virgen de la Victoria (Màlaga, Andalusia))
Angona, Anna (Institut Català d'Oncologia)
Cuevas, Beatriz (Hospital Universitario de Burgos)
Senín, María-Alicia (Institut Català d'Oncologia)
Ramírez-Payer, Angel (Hospital Universitario Central de Asturias)
Ramírez, María-José (Hospital General (Jerez de la Frontera, Espanya))
Pérez-López, Raúl (Hospital Universitario Virgen de la Arrixaca (Múrcia))
González de Villambrosía, Sonia (Hospital Universitario Marqués de Valdecilla (Santander, Cantabria))
Martínez-Valverde, Clara (Institut d'Investigació Biomèdica Sant Pau)
Gómez-Casares, María-Teresa (Hospital Dr Negrín (Las Palmas de Gran Canaria, Espanya))
García-Hernández, Carmen (Hospital General Universitario de Alicante (Alacant, País Valencià))
Gasior, Mercedes (Hospital Universitario La Paz (Madrid))
Bellosillo Paricio, Beatriz (Hospital del Mar (Barcelona, Catalunya))
Steegmann, Juan-Luis (Hospital de La Princesa (Madrid, Spain))
Álvarez-Larrán, Alberto (Hospital Clínic i Provincial de Barcelona)
Hernández Rivas, Jesús María (Hospital Clínico Universitario (Salamanca))
Hernandez-Boluda, Juan Carlos (Hospital Clínic Universitari (València))
Universitat Autònoma de Barcelona

Date:	2022
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.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Published in:	HemaSphere, Vol. 7 (december 2022) , ISSN 2572-9241

DOI: 10.1097/HS9.0000000000000818
PMID: 36570691

10 p, 3.5 MB

The record appears in these collections:
Research literature > UAB research groups literature > Research Centres and Groups (research output) > Health sciences and biosciences > Institut d'Investigació en Ciencies de la Salut Germans Trias i Pujol (IGTP) > Josep Carreras Leukaemia Research Institute
Research literature > UAB research groups literature > Research Centres and Groups (research output) > Health sciences and biosciences > Institut de Recerca Sant Pau
Articles > Research articles
Articles > Published articles