Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms : An Analysis of the Spanish Group of Myelodysplastic Syndromes
Mosquera Orgueira, Adrian (Complexo Hospitalario Universitario de Santiago de Compostela. Department of Hematology)
Perez Encinas, Manuel Mateo (Complexo Hospitalario Universitario de Santiago de Compostela. Department of Hematology)
Diaz Varela, Nicolas (Hospital Universitario Central de Asturias)
Mora, Elvira (Hospital Universitari i Politècnic La Fe (València))
Díaz-Beyá, Marina (Hospital Clínic i Provincial de Barcelona)
Montoro, María Julia (Vall d'Hebron Institut d'Oncologia)
Pomares, Helena (Hematology Department. Hospital Duran i Reynals)
Ramos, Fernando (Department of Hematology. Hospital Universitario de León)
Tormo, Mar (Hospital Clínic Universitari (València))
Jerez, Andres (Hospital General Universitario Morales Meseguer (Múrcia))
Nomdedeu, Josep F. (Institut d'Investigació Biomèdica Sant Pau)
De Miguel Sanchez, Carlos (Arabako Unibertsitate Ospitalea (Vitoria, País Basc))
Leonor, Arenillas (Institut Hospital del Mar d'Investigacions Mèdiques)
Cárcel, Paula (Department of Hematology. Hospital Público Universitario de la Ribera)
Cedena Romero, María Teresa (Hospital 12 de Octubre (Madrid))
Xicoy, Blanca (Institut Germans Trias i Pujol. Institut de Recerca contra la Leucèmia Josep Carreras)
Rivero, Eugenia (Department of Hematology. University Hospital Arnau de Vilanova)
Del Orbe Barreto, Rafael Andres (Hospital Universitario de Cruces (Barakaldo, País Basc))
Diez-Campelo, María (Hospital Universitario de Salamanca)
Benlloch, Luis E. (Grupo Español de Síndromes Mielodisplásicos)
Crucitti, Davide (Instituto de Investigacions Sanitarias de Santiago de Compostela)
Valcárcel, David (Vall d'Hebron Institut d'Oncologia)

Date:	2023
Abstract:	Myelodysplastic neoplasms (MDS) are a heterogeneous group of hematological stem cell disorders characterized by dysplasia, cytopenias, and increased risk of acute leukemia. As prognosis differs widely between patients, and treatment options vary from observation to allogeneic stem cell transplantation, accurate and precise disease risk prognostication is critical for decision making. With this aim, we retrieved registry data from MDS patients from 90 Spanish institutions. A total of 7202 patients were included, which were divided into a training (80%) and a test (20%) set. A machine learning technique (random survival forests) was used to model overall survival (OS) and leukemia-free survival (LFS). The optimal model was based on 8 variables (age, gender, hemoglobin, leukocyte count, platelet count, neutrophil percentage, bone marrow blast, and cytogenetic risk group). This model achieved high accuracy in predicting OS (c-indexes; 0. 759 and 0. 776) and LFS (c-indexes; 0. 812 and 0. 845). Importantly, the model was superior to the revised International Prognostic Scoring System (IPSS-R) and the age-adjusted IPSS-R. This difference persisted in different age ranges and in all evaluated disease subgroups. Finally, we validated our results in an external cohort, confirming the superiority of the Artificial Intelligence Prognostic Scoring System for MDS (AIPSS-MDS) over the IPSS-R, and achieving a similar performance as the molecular IPSS. In conclusion, the AIPSS-MDS score is a new prognostic model based exclusively on traditional clinical, hematological, and cytogenetic variables. AIPSS-MDS has a high prognostic accuracy in predicting survival in MDS patients, outperforming other well-established risk-scoring systems.
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 Núm. 10 (november 2023) , p. E961, ISSN 2572-9241

DOI: 10.1097/HS9.0000000000000961
PMID: 37841754

10 p, 3.8 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
Articles > Research articles
Articles > Published articles