ArticleCancer medicine2019
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.
Article in Cancer medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review.BMC medical research methodology · 2022Pooled it
- A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT).Sensors (Basel, Switzerland) · 2020Pooled it
- Putting the I in AML: Artificial Intelligence and Machine Learning in Acute Myeloid Leukemia.Cells · 2026Review
- Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation.Computational and structural biotechnology journal · 2026Review
- Advancements and Challenges in Salivary Metabolomics for Early Detection and Monitoring of Systemic Diseases.MedComm · 2025Review
- The Applications of Machine Learning in the Management of Patients Undergoing Stem Cell Transplantation: Are We Ready?Cancers · 2025Review
- Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care.JMIR mHealth and uHealth · 2024Article
- 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
- Unlocking the Potential of Artificial Intelligence in Acute Myeloid Leukemia and Myelodysplastic Syndromes.Current hematologic malignancy reports · 2024Review
- Optimization of diagnosis and treatment of hematological diseases via artificial intelligence.Frontiers in medicine · 2024Review
- A pilot study of implication of machine learning for relapse prediction after allogeneic stem cell transplantation in adults with Ph-positive acute lymphoblastic leukemia.Scientific reports · 2023Article
- A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects.Journal of medical Internet research · 2022Review
- Predicting Long-term Survival After Allogeneic Hematopoietic Cell Transplantation in Patients With Hematologic Malignancies: Machine Learning-Based Model Development and Validation.JMIR medical informatics · 2022Article
- 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 Clinical Prognostic Model Based on Machine Learning from the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial.Cancers · 2021Article
- Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects.Blood advances · 2020Review
- Identification of significant risks in pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) approach.Medical & biological engineering & computing · 2020Article
- 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
16 authors.
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Abstract
Although allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a curative therapy for high-risk acute leukemia (AL), some patients still relapse. Since patients simultaneously have many prognostic factors, difficulties are associated with the construction of a patient-based prediction algorithm of relapse. The alternating decision tree (ADTree) is a successful classification method that combines decision trees with the predictive accuracy of boosting. It is a component of machine learning (ML) and has the capacity to simultaneously analyze multiple factors. Using ADTree, we attempted to construct a prediction model of leukemia relapse within 1 year of transplantation. With the model of training data (n = 148), prediction accuracy, the AUC of ROC, and the κ-statistic value were 78.4%, 0.746, and 0.508, respectively. The false positive rate (FPR) of the relapse prediction was as low as 0.134. In an evaluation of the model with validation data (n = 69), prediction accuracy, AUC, and FPR of the relapse prediction were similar at 71.0%, 0.667, and 0.216, respectively. These results suggest that the model is generalized and highly accurate. Furthermore, the output of ADTree may visualize the branch point of treatment. For example, the selection of donor types resulted in different relapse predictions. Therefore, clinicians may change treatment options by referring to the model, thereby improving outcomes. The present results indicate that ML, such as ADTree, will contribute to the decision-making process in the diversified allo-HSCT field and be useful for preventing the relapse of leukemia.
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