Evaluating Molecular and Clinical Predictors in Myelodysplastic Syndromes through Machine Learning Integration
Mosquera Orgueira A, Perez Encinas M, Pérez Míguez C, Crucitti D, Piñeiro Fiel M, Díaz Varela N, Mora E, Díaz-Beyá M, Montoro M, Pomares H, Ramos Ortega F, Tormo M, Jerez A, Nomdedeu J, de Miguel Sanchez C, Arenillas L, Carcel Corella P, Cedena Romero M, Xicoy B, Rivero M, Del Orbe Barreto R, Bewersdorf J, Stahl M, Stempel J, Kewan T, Zeidan A, Diez-Campelo M, Valcarcel D. Evaluating Molecular and Clinical Predictors in Myelodysplastic Syndromes through Machine Learning Integration. Blood 2024, 144: 6683-6683. DOI: 10.1182/blood-2024-198114.Peer-Reviewed Original ResearchLeukemia-free survivalPredictors of OSMyelodysplastic syndromeOverall survivalIPSS-MC-indexFLT3-ITDPredictors of leukemia-free survivalRevised International Prognostic Scoring SystemPrognostication of myelodysplastic syndromesInternational Prognostic Scoring SystemPrognostic Scoring SystemCox regression analysisMolecular dataClinical trial designIPSS-RGenetic variabilityMLL-PTDRandom survival forestPrognostic landscapePrognostic significanceLaboratory parametersAffected prognosisClinical predictorsClinical indices