ArticlePloS one2016
Prediction of Hematopoietic Stem Cell Transplantation Related Mortality- Lessons Learned from the In-Silico Approach: A European Society for Blood and Marrow Transplantation Acute Leukemia Working Party Data Mining Study.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT).Sensors (Basel, Switzerland) · 2020Pooled it
- Development and Validation of Prognostic Models for Treatment Response of Patients with B-Cell Lymphoma: Standard Statistical and Machine-Learning Approaches.Journal of clinical medicine · 2025Article
- Synthetic Tabular Data Generation Under Horizontal Federated Learning Environments in Acute Myeloid Leukemia: Case-Based Simulation Study.JMIR medical informatics · 2025Article
- Application of Artificial Intelligence in Inborn Errors of Immunity Identification and Management: Past, Present, and Future-A Systematic Review.Journal of clinical medicine · 2025Review
- Survival Prediction in Allogeneic Haematopoietic Stem Cell Transplant Recipients Using Pre- and Post-Transplant Factors and Computational Intelligence.Journal of cellular and molecular medicine · 2025Article
- Article
- The Applications of Machine Learning in the Management of Patients Undergoing Stem Cell Transplantation: Are We Ready?Cancers · 2025Review
- Machine learning-based prediction of one-year mortality after alloHCT identifies the impact of pre-transplant immunity and inflammation.Frontiers in immunology · 2025Article
- Exploring Pattern of Relapse in Pediatric Patients with Acute Lymphocytic Leukemia and Acute Myeloid Leukemia Undergoing Stem Cell Transplant Using Machine Learning Methods.Journal of clinical medicine · 2024Article
- Deep Learning in Hematology: From Molecules to Patients.Clinical hematology international · 2024Review
- Nomogram for Predicting Early Mortality after Umbilical Cord Blood Transplantation in Children with Inborn Errors of Immunity.Journal of clinical immunology · 2023Article
- Current Status and Perspectives of Allogeneic Hematopoietic Stem Cell Transplantation in Elderly Patients with Acute Myeloid Leukemia.Stem cells translational medicine · 2022Review
- Establishment of a predictive model for GVHD-free, relapse-free survival after allogeneic HSCT using ensemble learning.Blood advances · 2022Article
- High lactate dehydrogenase at time of admission for allogeneic hematopoietic transplantation associates to poor survival in acute myeloid leukemia and non-Hodgkin lymphoma.Bone marrow transplantation · 2021Article
- Translational Applications of Artificial Intelligence and Machine Learning for Diagnostic Pathology in Lymphoid Neoplasms: A Comprehensive and Evolutive Analysis.Biomolecules · 2021Article
- Machine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases.Clinical hematology international · 2021Review
- Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects.Blood advances · 2020Review
- Acute myeloid leukemia and artificial intelligence, algorithms and new scores.Best practice & research. Clinical haematology · 2020Review
- Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation.Blood advances · 2019Article
- Patient-based prediction algorithm of relapse after allo-HSCT for acute Leukemia and its usefulness in the decision-making process using a machine learning approach.Cancer medicine · 2019Article
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Authors and funding
13 authors.
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Abstract
Models for prediction of allogeneic hematopoietic stem transplantation (HSCT) related mortality partially account for transplant risk. Improving predictive accuracy requires understating of prediction limiting factors, such as the statistical methodology used, number and quality of features collected, or simply the population size. Using an in-silico approach (i.e., iterative computerized simulations), based on machine learning (ML) algorithms, we set out to analyze these factors. A cohort of 25,923 adult acute leukemia patients from the European Society for Blood and Marrow Transplantation (EBMT) registry was analyzed. Predictive objective was non-relapse mortality (NRM) 100 days following HSCT. Thousands of prediction models were developed under varying conditions: increasing sample size, specific subpopulations and an increasing number of variables, which were selected and ranked by separate feature selection algorithms. Depending on the algorithm, predictive performance plateaued on a population size of 6,611-8,814 patients, reaching a maximal area under the receiver operator characteristic curve (AUC) of 0.67. AUCs' of models developed on specific subpopulation ranged from 0.59 to 0.67 for patients in second complete remission and receiving reduced intensity conditioning, respectively. Only 3-5 variables were necessary to achieve near maximal AUCs. The top 3 ranking variables, shared by all algorithms were disease stage, donor type, and conditioning regimen. Our findings empirically demonstrate that with regards to NRM prediction, few variables "carry the weight" and that traditional HSCT data has been "worn out". "Breaking through" the predictive boundaries will likely require additional types of inputs.
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