Garuffo Luca, Leoni Alessandro, Gatta Roberto, Bernardi Simona
Unit of Blood Disease and Stem Cell Transplantation, Department of Clinical and Experimental Sciences, University of Brescia, ASST Spedali Civili, 25123 Brescia, Italy.
CREA (Centro di Ricerca Emato-Oncologica AIL), ASST Spedali Civili of Brescia, 25123 Brescia, Italy.
Cancers (Basel). 2025 Jan 25;17(3):395. doi: 10.3390/cancers17030395.
Hematopoietic stem cell transplantation (HSCT) is a life-saving therapy for hematologic malignancies, such as leukemia and lymphoma and other severe conditions but is associated with significant risks, including graft versus host disease (GVHD), relapse, and treatment-related mortality. The increasing complexity of clinical, genomic, and biomarker data has spurred interest in machine learning (ML), which has emerged as a transformative tool to enhance decision-making and optimize outcomes in HSCT. This review examines the applications of ML in HSCT, focusing on donor selection, conditioning regimen, and prediction of post-transplant outcomes. Machine learning approaches, including decision trees, random forests, and neural networks, have demonstrated potential in improving donor compatibility algorithms, mortality and relapse prediction, and GVHD risk stratification. Integrating "omics" data with ML models has enabled the identification of novel biomarkers and the development of highly accurate predictive tools, supporting personalized treatment strategies. Despite promising advancements, challenges persist, including data standardization, algorithm interpretability, and ethical considerations regarding patient privacy. While ML holds promise for revolutionizing HSCT management, addressing these barriers through multicenter collaborations and regulatory frameworks remains essential for broader clinical adoption. In addition, the potential of ML can cope with some challenges such as data harmonization, patients' data protection, and availability of adequate infrastructure. Future research should prioritize larger datasets, multimodal data integration, and robust validation methods to fully realize ML's transformative potential in HSCT.
造血干细胞移植（HSCT）是治疗白血病、淋巴瘤等血液系统恶性肿瘤及其他严重疾病的一种挽救生命的疗法，但它也伴随着重大风险，包括移植物抗宿主病（GVHD）、复发和治疗相关死亡率。临床、基因组和生物标志物数据的日益复杂激发了人们对机器学习（ML）的兴趣，机器学习已成为一种变革性工具，可增强造血干细胞移植中的决策制定并优化治疗结果。本文综述探讨了机器学习在造血干细胞移植中的应用，重点关注供体选择、预处理方案以及移植后结果的预测。包括决策树、随机森林和神经网络在内的机器学习方法，在改进供体相容性算法、死亡率和复发预测以及移植物抗宿主病风险分层方面已显示出潜力。将“组学”数据与机器学习模型相结合，能够识别新的生物标志物并开发高度准确的预测工具，支持个性化治疗策略。尽管取得了有前景的进展，但挑战依然存在，包括数据标准化、算法可解释性以及患者隐私方面的伦理考量。虽然机器学习有望彻底改变造血干细胞移植管理，但通过多中心合作和监管框架来克服这些障碍，对于更广泛的临床应用仍然至关重要。此外，机器学习的潜力可以应对一些挑战，如数据协调、患者数据保护和充足基础设施的可用性。未来的研究应优先考虑更大的数据集、多模态数据整合以及强大的验证方法，以充分实现机器学习在造血干细胞移植中的变革潜力。
