使用机器学习算法预测转移性乳腺癌患者乳腺癌特异性生存率
Yufan Feng1, Natasha McGuire1, Alexandra Walton1,2
1UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane 4029, Australia.
Journal of pathology informatics
|September 4, 2023
概括
机器学习模型可以预测转移性乳腺癌 (MpBC) 的存活率,这是一个罕见且具有攻击性的亚型. 随机森林模型在预测乳腺癌特异性生存率 (BCSS) 中显示出最高的准确性.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 机器学习在医学中的应用
背景情况:
- 转移性乳腺癌 (MpBC) 是一种罕见的,激进的乳腺癌亚型.
- 关于MpBC的预后因素和生存预测的数据有限.
- 准确的预后对于患者管理至关重要.
研究的目的:
- 开发和评估机器学习模型,用于预测乳腺癌患者的乳腺癌特异性生存率 (BCSS).
- 确定在MpBC中预测BCSS的关键变量.
- 评估不同的机器学习算法对MpBC预后的有用性.
主要方法:
- 使用了160名MpBC患者的数据集,其中包括临床,病理和生物变量.
- 使用增益比率和基于相关性的方法进行了深入的变量选择,确定了10个关键变量.
- 通过使用十倍交叉验证评估了五种机器学习模型 (带包装的决策树,物流回归,多层感知子,天真贝叶斯,随机森林).
主要成果:
- 随机森林模型在预测BCSS方面取得了最高的表现,ROC面积为0.808.8.
- 变量选择确定了BCSS的10个显著预测因素.
- 该研究表明了机器学习的潜力,尽管缺乏治疗数据.
结论:
- 机器学习算法显示出预测罕见和异质癌症亚型 (如MpBC) 的预后的巨大潜力.
- 随机森林模型是预测MBCBC患者的BCSS的一个有希望的工具.
- 未来的研究应该包括治疗数据和先进的ML技术,以改善预后模型.
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