在急性白血病患者中,使用机器学习技术进行全源造血干细胞移植的预测性结果建模
Maedeh Rouzbahani1, Seyed Amirhossein Mousavi2, Ghasem Hajianfar3
1Tehran University of Medical Sciences, School of Medicine, Tehran, Iran; Advanced Diagnostic and Interventional Radiology Research Center (ADIR) Tehran University of Medical Science, Tehran, Iran.
Leukemia research
|November 26, 2024
概括
机器学习模型准确地预测干细胞移植后急性白血病患者的结果. 这些模型预测了整体存活率,复发率和移植对宿主疾病,改善了患者护理策略.
科学领域:
- 血液学 血液学 血液学
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 治疗急性白血病 (AL) 需要对有效疗法的持续研究.
- 异构造造血干细胞移植 (allo-HSCT) 是AL的关键治疗方法,其关键指标是总体存活率 (OS),复发和移植与宿主疾病 (GVHD) 等结果.
研究的目的:
- 评估机器学习 (ML) 模型对AL患者在allo-HSCT后的OS,复发和GVHD的预测能力.
- 确定最佳的ML模型和特征选择方法来预测这些关键结果.
主要方法:
- 开发28个ML模型,使用来自1243名AL患者的临床数据,随访10年.
- 整合了四种特征选择技术与七种ML算法.
- 使用一致性指数 (c-index) 和多变量分析进行性能评估.
主要成果:
- 特定特征选择和ML算法组合显示出显著的预测能力 (p < 0.0001).
- 确定了用于预测OS (例如UCI_GLMN,IBMA_GLMN),复发 (例如UCI_ST,UCI_RSF),急性GVHD (IBMA_GB) 和慢性GVHD (Boruta_GB) 的顶级模型.
- 获得的平均c指数范围从0.61到0.68.
结论:
- 机器学习模型,当与临床数据集成时,在预测AL患者接受allo-HSCT的关键结果时提供高准确性.
- 这些发现支持ML在完善预后评估和指导白血病护理治疗决策方面的潜力.
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