ArticleBlood2025
Use of machine learning techniques to predict poor survival after hematopoietic cell transplantation for myelofibrosis.
Article in Blood, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Putting the I in AML: Artificial Intelligence and Machine Learning in Acute Myeloid Leukemia.Cells · 2026Review
- Machine learning-guided risk stratification in elderly AML based on genomic, immunophenotypic and therapeutic profiles.BMC geriatrics · 2026Article
- Prognostic Scores and Risk Stratification of Myeloproliferative Neoplasms: 2026 Updates.Cancers · 2026Review
- Explainable machine-learning prediction of overall and cancer-specific survival in adult triple-negative breast cancer using SEER: a comparative study of nomograms and random survival forests.Translational cancer research · 2026Article
- Treosulfan-based double-alkylator conditioning in myelofibrosis: An Italian retrospective multicenter analysis.Bone marrow transplantation · 2026Article
- Health Care Utilization Databases obtained from health system inform outcome for ruxolitinib treatment in patients with myelofibrosis.HemaSphere · 2026Article
- Machine learning-driven dynamic prognosis for primary colorectal lymphoma.Scientific reports · 2026Article
- Development of a stacked ensemble model for risk stratification of chronic GVHD after allogeneic HSCT: Japanese nation-wide cohort study.Frontiers in immunology · 2026Article
- Artificial intelligence for risk assessment and outcome prediction in malignant haematology.British journal of haematology · 2026Review
- Precision medicine in myelofibrosis: from molecular profiling to personalized therapy.Frontiers in oncology · 2026Review
- Designing a web-based platform for dynamic estimation of individualized conditional survival in grade 3 gliomas.Discover oncology · 2025Article
- Article
- Optimization of allogeneic hematopoietic cell transplantation for patients with myelofibrosis treated with ruxolitinib: eligibility, best practices, and improving transplant outcomes.Annals of hematology · 2025Review
- Machine learning-based prediction of one-year mortality after alloHCT identifies the impact of pre-transplant immunity and inflammation.Frontiers in immunology · 2025Article
- Machine learning-based prognostic modeling for locally advanced non-small cell lung cancer treated with immuno-radiotherapy.Frontiers in pharmacology · 2025Article
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30 authors.
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No grant is acknowledged in the PubMed record.
Abstract
abstractWith 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.
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