ArticleBlood advances2022
Establishment of a predictive model for GVHD-free, relapse-free survival after allogeneic HSCT using ensemble learning.
Article in Blood advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Efficacy and Safety of Combining r-ATG With PTCy for GvHD Prophylaxis in Haploidentical HSC Transplantation for Malignancies-A Systematic Review.Journal of cellular and molecular medicine · 2025Pooled it
- Addressing the translation gap in SCT donor selection.Frontiers in immunology · 2026Review
- Development of a stacked ensemble model for risk stratification of chronic GVHD after allogeneic HSCT: Japanese nation-wide cohort study.Frontiers in immunology · 2026Article
- Article
- The Applications of Machine Learning in the Management of Patients Undergoing Stem Cell Transplantation: Are We Ready?Cancers · 2025Review
- High-dimensional Immune Profiles and Machine Learning May Predict Acute Myeloid Leukemia Relapse Early following Transplant.Journal of immunology (Baltimore, Md. : 1950) · 2024Article
- Graft-versus-host disease and impact on relapse in myelofibrosis undergoing hematopoietic stem cell transplantation.Bone marrow transplantation · 2024Article
- Planning GvHD preemptive therapy: risk factors, biomarkers, and prognostic scores.Hematology. American Society of Hematology. Education Program · 2023Article
- A convolutional neural network-based model that predicts acute graft-versus-host disease after allogeneic hematopoietic stem cell transplantation.Communications medicine · 2023Article
- Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chi-Square Test and Hyperparameter Optimization: A Retrospective Analysis.Computational and mathematical methods in medicine · 2022Article
- A Risk-Factor Model for Antineoplastic Drug-Induced Serious Adverse Events in Cancer Inpatients: A Retrospective Study Based on the Global Trigger Tool and Machine Learning.Frontiers in pharmacology · 2022Article
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Authors and funding
24 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Graft-versus-host disease-free, relapse-free survival (GRFS) is a useful composite end point that measures survival without relapse or significant morbidity after allogeneic hematopoietic stem cell transplantation (allo-HSCT). We aimed to develop a novel analytical method that appropriately handles right-censored data and competing risks to understand the risk for GRFS and each component of GRFS. This study was a retrospective data-mining study on a cohort of 2207 adult patients who underwent their first allo-HSCT within the Kyoto Stem Cell Transplantation Group, a multi-institutional joint research group of 17 transplantation centers in Japan. The primary end point was GRFS. A stacked ensemble of Cox Proportional Hazard (Cox-PH) regression and 7 machine-learning algorithms was applied to develop a prediction model. The median age for the patients was 48 years. For GRFS, the stacked ensemble model achieved better predictive accuracy evaluated by C-index than other state-of-the-art competing risk models (ensemble model: 0.670; Cox-PH: 0.668; Random Survival Forest: 0.660; Dynamic DeepHit: 0.646). The probability of GRFS after 2 years was 30.54% for the high-risk group and 40.69% for the low-risk group (hazard ratio compared with the low-risk group: 2.127; 95% CI, 1.19-3.80). We developed a novel predictive model for survival analysis that showed superior risk stratification to existing methods using a stacked ensemble of multiple machine-learning algorithms.
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