Predictive Modeling of Outcomes in Acute Leukemia Patients Undergoing Allogeneic Hematopoietic Stem Cell Transplantation Using Machine Learning Techniques

Predictive Modeling of Outcomes in Acute Leukemia Patients Undergoing Allogeneic Hematopoietic Stem Cell Transplantation Using Machine Learning Techniques Publisher Pubmed

Rouzbahani M^{1, 2} ; Mousavi SA³ ; Hajianfar G⁴ ; Ghanaati A⁵ ; Vaezi M¹ ; Ghavamzadeh A¹ ; Barkhordar M⁶

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1. Tehran University of Medical Sciences, School of Medicine, Tehran, Iran
2. Advanced Diagnostic and Interventional Radiology Research Center (ADIR) Tehran University of Medical Science, Tehran, Iran
3. Department of Medical Physics, Kashan University of Medical Sciences, Kashan, Iran
4. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
5. Shahid Beheshti University of Medical Sciences, School of Allied Medical Sciences, Tehran, Iran
6. Cell Therapy and Hematopoietic Stem Cell Transplantation Research Center, Research Institute for Oncology, Hematology and Cell Therapy, Iran

Source: Leukemia Research Published:2025

Abstract

Background: Leukemia necessitates continuous research for effective therapeutic techniques. Acute leukemia (AL) patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT) focus on key outcomes such as overall survival (OS), relapse, and graft-versus-host disease (GVHD). Objective: This study aims to evaluate the capability of machine learning (ML) models in predicting OS, relapse, and GVHD in AL patients post-allo-HSCT. Methods: Clinical data from 1243 AL patients, with 10 years of follow-up, was utilized to develop 28 ML models. These models incorporated four feature selection methods and seven ML algorithms. Model performance was assessed using the concordance index (c-index) with multivariate analysis. Results: The multivariate model analysis showed the best FS/ML combinations were UCI_GLMN, IBMA_GLMN and IBMA_CB for OS, UCI_ST, UCI_RSF, UCI GLMB, UCI_GB, UCI_CB, MI_GLMN, IBMA_ST and IBMA GB for relapse, IBMA_GB for aGVHD and Boruta_GB for cGVHD (all p values < 0.0001, mean C-indices in 0.61–0.68)). Conclusion: ML techniques, when combined with clinical variables, demonstrate high accuracy in predicting OS, relapse, and GVHD in AL patients. © 2024 Elsevier Ltd

Style	Citing Format
MLA	Rouzbahani M, et al.. "Predictive Modeling of Outcomes in Acute Leukemia Patients Undergoing Allogeneic Hematopoietic Stem Cell Transplantation Using Machine Learning Techniques." Leukemia Research, vol. 148, no. , 2025, pp. -.
APA	Rouzbahani M, Mousavi SA, Hajianfar G, Ghanaati A, Vaezi M, Ghavamzadeh A, Barkhordar M (2025). Predictive Modeling of Outcomes in Acute Leukemia Patients Undergoing Allogeneic Hematopoietic Stem Cell Transplantation Using Machine Learning Techniques. Leukemia Research, 148(), -.
Chicago	Rouzbahani M, Mousavi SA, Hajianfar G, Ghanaati A, Vaezi M, Ghavamzadeh A, Barkhordar M. "Predictive Modeling of Outcomes in Acute Leukemia Patients Undergoing Allogeneic Hematopoietic Stem Cell Transplantation Using Machine Learning Techniques." Leukemia Research 148, no. (2025): -.
Harvard	Rouzbahani M et al. (2025) 'Predictive Modeling of Outcomes in Acute Leukemia Patients Undergoing Allogeneic Hematopoietic Stem Cell Transplantation Using Machine Learning Techniques', Leukemia Research, 148(), pp. -.
Vancouver	Rouzbahani M, Mousavi SA, Hajianfar G, Ghanaati A, Vaezi M, Ghavamzadeh A, et al.. Predictive Modeling of Outcomes in Acute Leukemia Patients Undergoing Allogeneic Hematopoietic Stem Cell Transplantation Using Machine Learning Techniques. Leukemia Research. 2025;148():-.
BibTex	@article{ author = {Rouzbahani M and Mousavi SA and Hajianfar G and Ghanaati A and Vaezi M and Ghavamzadeh A and Barkhordar M}, title = {Predictive Modeling of Outcomes in Acute Leukemia Patients Undergoing Allogeneic Hematopoietic Stem Cell Transplantation Using Machine Learning Techniques}, journal = {Leukemia Research}, volume = {148}, number = {}, pages = {-}, year = {2025} }
RIS	TY - JOUR AU - Rouzbahani M AU - Mousavi SA AU - Hajianfar G AU - Ghanaati A AU - Vaezi M AU - Ghavamzadeh A AU - Barkhordar M TI - Predictive Modeling of Outcomes in Acute Leukemia Patients Undergoing Allogeneic Hematopoietic Stem Cell Transplantation Using Machine Learning Techniques JO - Leukemia Research VL - 148 IS - SP - EP - PY - 2025 ER -

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