乳腺癌复发风险预测的演变:基于统计和机器学习的模型的系统审查.
Hasna El Haji1,2,3, Amine Souadka4, Bhavik N Patel1,2
1Department of Radiology, Mayo Clinic, Phoenix, AZ.
JCO clinical cancer informatics
|August 11, 2023
概括
这项研究审查了乳腺癌复发预测模型,发现机器学习 (ML) 模型,特别是深度学习,提供了高准确度. 然而,在解释性,数据局限性,以及在模型开发中不同人群的代表性不足等方面仍然存在挑战.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 准确的乳腺癌 (BC) 复发预测对于有效的辅助治疗选择至关重要.
- 已经开发了许多统计和机器学习 (ML) 模型,其复杂性和预测能力不断提高.
研究的目的:
- 系统地审查和基准统计和基于ML的乳腺癌复发预测模型.
- 评估模型开发,绩效指标,验证策略和种族多样性.
主要方法:
- 在五个主要数据库中,按照PRISMA指南进行了系统的文献审查.
- 分析了2008年至2022年间发表的23篇相关论文,重点是用于BC复发预测的统计/ML方法.
- 研究对开发,评估,验证和患者的种族多样性进行了基准评估.
主要成果:
- 机器学习 (ML) 模型占主导地位,有69.6%的审查研究使用ML技术.
- 深度学习和集体学习模型显示出最高的预测准确性 (AUC = 0.94).
- 传统的ML模型和深度学习是最常用的技术.
结论:
- 机器学习模型显示乳腺癌复发预测的高准确性,但在解释性和概括性方面面临挑战.
- 有限的变量包含,不平衡的数据集和缺乏开源数据阻碍了模型开发和验证.
- 现有的模型主要缺乏多样性,在高加索和亚洲人群上接受培训和验证,忽视了其他种族.
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