With the incorporation of effective therapies for myelofibrosis (MF), accurately predicting outcomes after allogeneic hematopoietic cell transplantation (allo-HCT) is crucial for determining the optimal timing for this procedure. Using data from 5183 patients with MF who underwent first allo-HCT between 2005 and 2020 at European Society for Blood and Marrow Transplantation centers, we examined different machine learning (ML) models to predict overall survival after transplant. The cohort was divided into a training set (75%) and a test set (25%) for model validation. A random survival forests (RSF) model was developed based on 10 variables: patient age, comorbidity index, performance status, blood blasts, hemoglobin, leukocytes, platelets, donor type, conditioning intensity, and graft-versus-host disease prophylaxis. Its performance was compared with a 4-level Cox regression-based score and other ML-based models derived from the same data set, and with the Center for International Blood and Marrow Transplant Research score. The RSF outperformed all comparators, achieving better concordance indices across both primary and postessential thrombocythemia/polycythemia vera MF subgroups. The robustness and generalizability of the RSF model was confirmed by Akaike information criterion and time-dependent receiver operating characteristic area under the curve metrics in both sets. Although all models were prognostic for nonrelapse mortality, the RSF provided better curve separation, effectively identifying a high-risk group comprising 25% of patients. In conclusion, ML enhances risk stratification in patients with MF undergoing allo-HCT, paving the way for personalized medicine. A web application (https://gemfin.click/ebmt) based on the RSF model offers a practical tool to identify patients at high risk for poor transplantation outcomes, supporting informed treatment decisions and advancing individualized care.

Ematologia e Terapie Cellulari IRCCS Ospedale Policlinico San Martino Genova Italy

European Group for Blood and Marrow Transplantation Leiden Study Unit Leiden The Netherlands

Hematology Department Central Clinical Hospital The Medical University of Warsaw Warsaw Poland

Hematology Department Centre Hospitalier Universitaire de Lille INSERM U1286 Infinite Lille France

Hematology Department Erasmus MC Cancer Institute Rotterdam The Netherlands

Hematology Department Federico 2 University of Naples Naples Italy

Hematology Department Hospital Clínico Universitario Instituto de Investigación Sanitaria del Hospital Clínico de Valencia University of Valencia Valencia Spain

Hematology Department Maria Skłodowska Curie National Research Institute of Oncology Gliwice Poland

Hematology Department Medical Clinic and Policlinic Leipzig Germany

Hematology Department Rigshospitalet Copenhagen Denmark

Hematology Department Saint Louis Hospital Bone Marrow Transplantation Unit Paris France

Hematology Department University College London Hospitals National Health Service Trust London United Kingdom

Hematology Department University Hospital Basel Basel Switzerland

Hematology Department University Hospital Essen Duesseldorf Germany

Hematology Department University Hospital of Santiago de Compostela Instituto de Investigación Sanitaria de Santiago de Compostela Santiago de Compostela Spain

Hematology Department University Hospital Technische Universität Dresden Dresden Germany

Hematology Department University Hospital Uppsala Uppsala Sweden

Hematology Department University Medical Center Hamburg Eppendorf Hamburg Germany

Hematology Department University Medical Centre Utrecht The Netherlands

Hematology Department University of Liege Liege Belgium

Hematology Department University of Muenster Muenster Germany

Institute of Hematology and Blood Transfusion Prague Czech Republic

Medizinische Klinik m S Hämatologie Onkologie und Tumorimmunologie Berlin Germany

Unit of Bone Marrow Transplantation Division of Hematology Fondazione IRCCS Policlinico San Matteo Pavia Italy

Blood. 2025 Jun 26;145(26):3068-3069. doi: 10.1182/blood.2025028816. PubMed

Zobrazit více v PubMed

Passamonti F, Mora B. Myelofibrosis. Blood. 2023;141(16):1954–1970. PubMed PMC

Tefferi A. Primary myelofibrosis: 2023 update on diagnosis, risk-stratification, and management. Am J Hematol. 2023;98(5):801–821. PubMed

Kröger N, Bacigalupo A, Barbui T, et al. Indication and management of allogeneic haematopoietic stem-cell transplantation in myelofibrosis: updated recommendations by the EBMT/ELN International Working Group. Lancet Haematol. 2024;11(1):e62–e74. PubMed

Vachhani P, Verstovsek S, Bose P. Disease modification in myelofibrosis: an elusive goal? J Clin Oncol. 2022;40(11):1147–1154. PubMed PMC

England J, Gupta V. Novel therapies vs hematopoietic cell transplantation in myelofibrosis: who, when, how? Hematol Am Soc Hematol Educ Program. 2021;2021(1):453–462. PubMed PMC

Maze D, Arcasoy MO, Henrie R, et al. Upfront allogeneic transplantation versus JAK inhibitor therapy for patients with myelofibrosis: a North American collaborative study [published correction appears in Bone Marrow Transplant. 2024;59(2):196-202] Bone Marrow Transpl. 2024;59(2):196–202. PubMed PMC

Gagelmann N, Ditschkowski M, Bogdanov R, et al. Comprehensive clinical-molecular transplant scoring system for myelofibrosis undergoing stem cell transplantation. Blood. 2019;133(20):2233–2242. PubMed

Tamari R, McLornan DP, Ahn KW, et al. A simple prognostic system in patients with myelofibrosis undergoing allogeneic stem cell transplantation: a CIBMTR/EBMT analysis. Blood Adv. 2023;7(15):3993–4002. PubMed PMC

McLornan D, Eikema DJ, Czerw T, et al. Trends in allogeneic haematopoietic cell transplantation for myelofibrosis in Europe between 1995 and 2018: a CMWP of EBMT retrospective analysis. Bone Marrow Transpl. 2021;56(9):2160–2172. PubMed

Hernández-Boluda JC, Pereira A, Alvarez-Larran A, et al. Predicting survival after allogeneic hematopoietic cell transplantation in myelofibrosis: performance of the myelofibrosis transplant scoring system (MTSS) and development of a new prognostic model. Biol Blood Marrow Transpl. 2020;26(12):2237–2244. PubMed

Mosquera-Orgueira A, Pérez-Encinas M, Hernández-Sánchez A, et al. Machine learning improves risk stratification in myelofibrosis: an analysis of the Spanish Registry of Myelofibrosis. Hemasphere. 2023;7(1) PubMed PMC

Mosquera-Orgueira A, Arellano-Rodrigo E, Garrote M, et al. Integrating AIPSS-MF and molecular predictors: a comparative analysis of prognostic models for myelofibrosis. Hemasphere. 2024;8(3) PubMed PMC

Bacigalupo A, Ballen K, Rizzo D, et al. Defining the intensity of conditioning regimens: working definitions. Biol Blood Marrow Transpl. 2009;15(12):1628–1633. PubMed PMC

Hernández-Boluda JC, Pereira A, Kröger N, et al. Determinants of survival in myelofibrosis patients undergoing allogeneic hematopoietic cell transplantation. Leukemia. 2021;35(1):215–224. PubMed

Polverelli N, Bonneville EF, de Wreede LC, et al. Impact of comorbidities and body mass index on the outcomes of allogeneic hematopoietic cell transplantation in myelofibrosis: a study on behalf of the Chronic Malignancies Working Party of EBMT. Am J Hematol. 2024;99(5):993–996. PubMed

Copelan E, Casper JT, Carter SL, et al. A scheme for defining cause of death and its application in the T cell depletion trial. Biol Blood Marrow Transpl. 2007;13(12):1469–1476. PubMed

Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1999;94(446):496–509.

R Foundation The R Project for Statistical Computing. https://www.r-project.org/

Therneau TM, Lumley T, Elizabeth A, Cynthia C. A package for survival analysis in R. https://CRAN.R-project.org/package=survival

Gerds TA. prodlim: product-limit estimation for censored event history analysis. https://CRAN.R-project.org/package=prodlim

Gray B. cmprsk: subdistribution analysis of competing risks. https://CRAN.R-project.org/package=cmprsk

Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Ann Appl Stat. 2008;2(3):841–860.

Jaeger BC, Long DL, Long DM, et al. Oblique random survival forests. Ann Appl Stat. 2019;13(3):1847–1883. PubMed PMC

Chen T, Guestrin C. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery; 2016. XGBoost: a scalable tree boosting system; pp. 785–794.

Chen Bingshu E. A package of deep neural network tools for probability models. https://CRAN.R-project.org/package=dnn Accessed 1 May 2024.

Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361–387. PubMed

Blanche P, Dartigues JF, Jacqmin-Gadda H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med. 2013;32(30):5381–5397. PubMed

Akaike H. A new look at the statistical model identification. IEEE Trans Automat Contr. 1974;19(6):716–723.

Wal WMvd, Geskus RB. ipw: an R package for inverse probability weighting. J Stat Softw. 2011;43(13):1–23.

Kröger N, Wolschke C, Gagelmann N. How I treat transplant-eligible patients with myelofibrosis. Blood. 2023;142(20):1683–1696. PubMed

Mussetti A, Rius-Sansalvador B, Moreno V, et al. Artificial intelligence methods to estimate overall mortality and non-relapse mortality following allogeneic HCT in the modern era: an EBMT-TCWP study. Bone Marrow Transpl. 2024;59(2):232–238. PubMed

Kröger N, Panagiota V, Badbaran A, et al. Impact of molecular genetics on outcome in myelofibrosis patients after allogeneic stem cell transplantation. Biol Blood Marrow Transpl. 2017;23(7):1095–1101. PubMed

Ali H, Aldoss I, Yang D, et al. MIPSS70+ v2.0 predicts long-term survival in myelofibrosis after allogeneic HCT with the Flu/Mel conditioning regimen. Blood Adv. 2019;3(1):83–95. PubMed PMC

Tamari R, Rapaport F, Zhang N, et al. Impact of high-molecular-risk mutations on transplantation outcomes in patients with myelofibrosis. Biol Blood Marrow Transpl. 2019;25(6):1142–1151. PubMed PMC