Enhancing Personalized Prognostic Assessment of Myelodysplastic Syndromes through a Multimodal and Explainable Deep Data Fusion Approach (MAGAERA)
Sauta E, Sartori F, Lanino L, Asti G, D'Amico S, Delleani M, Riva E, Zampini M, Zazzetti E, Bicchieri M, Maggioni G, Campagna A, Todisco G, Tentori C, Ubezio M, Russo A, Buizza A, Ficara F, Crisafulli L, Brindisi M, Ventura D, Pinocchio N, Rahal D, Lancellotti C, Bonometti A, Di Tommaso L, Savevski V, Santoro A, Derus N, Dall'Olio D, Santini V, Sole F, Platzbecker U, Fenaux P, Diez-Campelo M, Komrokji R, Garcia-Manero G, Haferlach T, Kordasti S, Zeidan A, Castellani G, Sanavia T, Fariselli P, Della Porta M. Enhancing Personalized Prognostic Assessment of Myelodysplastic Syndromes through a Multimodal and Explainable Deep Data Fusion Approach (MAGAERA). Blood 2024, 144: 105-105. DOI: 10.1182/blood-2024-205413.Peer-Reviewed Original ResearchPersonalized medicine programsMyelodysplastic syndrome patientsMyelodysplastic syndromeOverall survivalConcordance indexClinical outcomesMay-Grunwald-GiemsaHypomethylating agentsBone marrowAnalysis of hematological malignanciesSomatic mutation screeningEvaluation of T lymphocytesResponse to hypomethylating agentsCD34+ bone marrowStudies of myelodysplastic syndromesGenomic featuresMDS populationRNA-seqPrediction of patient outcomeGenomic characterizationHarrell's concordance indexPredicting clinical outcomesHematoxylin and eosin (H&EMorphological dataMulti-omicsArtificial-Intelligence, Data-Driven, Comprehensive Classification of Myeloid Neoplasms Based on Genomic, Morphological and Histological Features
Lanino L, D'Amico S, Maggioni G, Al Ali N, Wang Y, Gurnari C, Gagelmann N, Bewersdorf J, Ball S, Guglielmelli P, Meggendorfer M, Hunter A, Kubasch A, Travaglino E, Campagna A, Ubezio M, Russo A, Todisco G, Tentori C, Buizza A, Sauta E, Zampini M, Riva E, Asti G, Delleani M, Ficara F, Santoro A, Sala C, Dall'Olio D, Dall'Olio L, Kewan T, Casetti I, Awada H, Xicoy B, Vucinic V, Hou H, Chou W, Yao C, Lin C, Tien H, Consagra A, Sallman D, Kern W, Bernardi M, Chiusolo P, Borin L, Voso M, Pleyer L, Palomo L, Quintela D, Jerez A, Cornejo E, Martin P, Díaz-Beyá M, Pita A, Roldan V, Suarez D, Velasco E, Calabuig M, Garcia-Manero G, Loghavi S, Platzbecker U, Sole F, Diez-Campelo M, Maciejewski J, Kröger N, Fenaux P, Fontenay M, Santini V, Haferlach T, Germing U, Padron E, Robin M, Passamonti F, Solary E, Vannucchi A, Castellani G, Zeidan A, Komrokji R, Della Porta M. Artificial-Intelligence, Data-Driven, Comprehensive Classification of Myeloid Neoplasms Based on Genomic, Morphological and Histological Features. Blood 2024, 144: 1005. DOI: 10.1182/blood-2024-204826.Peer-Reviewed Original ResearchGenomic featuresSplicing mutationBiallelic inactivationAnalysis of genomic profilesBiallelic inactivation of TP53Clinical phenotypeGene expression profilesCNV analysisMorphological featuresInactivation of TP53Myeloid neoplasmsGenomic characterizationRNAseq dataMorphological dataMutation screeningExpression profilesMutationsJAK/STATGenomic profilingGenomeHierarchical importanceHeterogeneous phenotypesIntegrated analysisPhenotypeHematological phenotype