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机器学习在预测血液癌症结果中的预测性表现:系统性审查和元分析

Adugnaw Zeleke Alem¹, Itismita Mohanty², Nalini Pati³

¹Health Research Institute, University of Canberra, Canberra, Australian Capital Territory, Australia; Department of Epidemiology and Biostatistics, College of Medicine and Health Sciences, Institute of Public Health, University of Gondar, Gondar, Ethiopia.

|August 1, 2025

概括

机器学习 (ML) 模型的性能与预测血液癌症结果的标准回归相似. 然而,外部验证的ML模型显著优于标准方法,尽管目前研究中的偏差风险很高.

科学领域:

血液学血液学血液学
医疗信息学医疗信息学
机器学习机器学习

背景情况:

血液性恶性瘤需要个性化治疗方法.
机器学习 (ML) 越来越多地被用于预测癌症的结果.
ML与标准回归方法的比较性能仍然不清楚.

研究的目的:

系统地审查和评估ML算法的性能与预测血液癌症结果的标准回归方法相比.
使用元分析评估ML模型的区分能力.

主要方法:

系统审查来自Web of Science,Medline,SCOPUS和CINHAL的研究.
使用预测模型偏差风险评估工具 (PROBAST) 进行偏差风险评估.
三级随机效应的元分析,以确定聚合的区分能力 (曲线下的面积 - AUC).

主要成果:

在表现最佳的ML模型中,聚合的AUC为0.80 (测试组).
在测试组中,ML算法显示了与标准回归相似的区分能力 (AUC=0.78对比0.72).
与标准模型 (AUC=0.72) 相比,外部验证的高性能ML模型显示出更高的性能 (AUC=0.87).

结论:

ML模型在预测血液癌症结果方面提供了可接受的性能.
外部验证的ML模型在标准回归上显示了显著的优势.
在现有研究中存在偏差的高风险,需要在未来的ML模型开发中遵守PROBAST指南.

关键词:

血液学癌症血液学癌症机器学习机器学习多层次的元分析.绩效指标是指性能指标.系统性审查系统性审查

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