Validation of Recent Response Criteria (ELN-22, IWG-23 and PB-CR) in 1634 MDS/CMML/AML Patients Treated with HMA or HMA-Ven Using CPH Models and a CPH Deep Neural Network - Can or Should Response Criteria be Harmonized for Similarly Treated Patients?
Pleyer L, Vaisband M, Klammer P, Drost M, Angermann H, Keil F, Petzer V, Heibl S, Moritz J, Girschikofsky M, Stampfl-Mattersberger M, Pichler A, Hartmann B, Aschauer G, Schmitt C, Vallet S, Boros S, Pichler P, Hammerl-Steiner A, Renneberg F, Majjiga D, Russ G, Egle A, Leisch M, Melchardt T, Zaborsky N, Faber V, Bewersdorf J, Zeidan A, Hasenauer J, Greil R. Validation of Recent Response Criteria (ELN-22, IWG-23 and PB-CR) in 1634 MDS/CMML/AML Patients Treated with HMA or HMA-Ven Using CPH Models and a CPH Deep Neural Network - Can or Should Response Criteria be Harmonized for Similarly Treated Patients? Blood 2024, 144: 7511-7511. DOI: 10.1182/blood-2024-208073.Peer-Reviewed Original ResearchComposite complete remissionTime to next treatmentCox proportional hazardsHypomethylating agentsOverall survivalCox proportional hazards modelsMedian OSResponse CriteriaTreated ptsCycles of HMAHigher-risk MDSKaplan-Meier methodBone marrow evaluationProspective cohort studyStandard of careComplete remissionMarrow evaluationTreated patientsMultivariable adjustmentNext treatmentCohort studyClinical trialsClinical overlapHazard ratioDisease entityFuture directions in myelodysplastic syndromes/neoplasms and acute myeloid leukaemia classification: from blast counts to biology
Della Porta M, Bewersdorf J, Wang Y, Hasserjian R. Future directions in myelodysplastic syndromes/neoplasms and acute myeloid leukaemia classification: from blast counts to biology. Histopathology 2024 PMID: 39450427, DOI: 10.1111/his.15353.Peer-Reviewed Original ResearchAcute myeloid leukemiaIntegration of genomic dataGenomic informationGenomic dataPresence of SF3B1 mutationsDisease entityMethylation profilesAML classificationRecurrent genetic abnormalitiesHaematopoietic cell proliferationPercentage of myeloblastsGene expressionGenetic featuresMultiple diagnostic modalitiesPatient risk stratificationSF3B1 mutationsBlast countDisease pathogenesisGenetic abnormalitiesCell proliferationTP53 abnormalitiesDiagnostic modalitiesMyeloid leukemiaBone marrowPatient cohortRisk prediction for clonal cytopenia: multicenter real-world evidence
Xie Z, Komrokji R, Al Ali N, Regelson A, Geyer S, Patel A, Saygin C, Zeidan A, Bewersdorf J, Mendez L, Kishtagari A, Zeidner J, Coombs C, Madanat Y, Chung S, Badar T, Foran J, Desai P, Tsai C, Griffiths E, Al Malki M, Amanam I, Lai C, Deeg H, Ades L, Arana Yi C, Osman A, Dinner S, Abaza Y, Taylor J, Chandhok N, Soong D, Brunner A, Carraway H, Singh A, Elena C, Ferrari J, Gallì A, Pozzi S, Padron E, Patnaik M, Malcovati L, Savona M, Al-Kali A. Risk prediction for clonal cytopenia: multicenter real-world evidence. Blood 2024, 144: 2033-2044. PMID: 38996210, PMCID: PMC11561536, DOI: 10.1182/blood.2024024756.Peer-Reviewed Original ResearchMyeloid neoplasmsIncidence of MNClonal cytopeniaCumulative incidencePlatelet count <High-risk mutationsCox proportional hazards modelsVariant allele fractionProportional hazards modelClinical trial designCCUS patientsStratify patientsGray's testC-indexDisease entityRisk groupsCytopeniasAllele fractionSomatic mutationsRisk factorsHigh riskNatural historyRisk scoreHazards modelPatients