一个多式机器学习模型用于乳腺癌风险分层
Xuejun Qian1,2,3, Jing Pei4,5, Chunguang Han5
1School of Biomedical Engineering, ShanghaiTech University, Shanghai, China. qianxj@shanghaitech.edu.cn.
Nature biomedical engineering
|December 5, 2024
概括
一种用于乳腺癌诊断的新型多式机器学习模型在分类瘤方面表现与放射科医生相似,在差异诊断方面具有更好的能力. 这种AI工具可能有助于瘤学家诊断癌症.
科学领域:
- 在瘤学中使用人工智能
- 医学成像分析 医学成像分析
- 机器学习用于医疗保健
背景情况:
- 机器学习 (ML) 模型可以帮助诊断乳腺癌,风险预测和患者管理.
- 临床整合需要模型来解释成像数据,使用临床信息,处理缺少的数据,并进行前性验证.
- 当前的诊断工作流可以从先进的计算工具中受益,以提高准确性和效率.
研究的目的:
- 开发和测试用于乳腺癌风险分层的多模式机器学习模型.
- 为了评估模型的性能与经验丰富的放射科医生在分类瘤和差异诊断.
- 为了评估模型的准确性在一个前性的设置使用活检乳腺样本.
主要方法:
- 开发一种多式模式,将乳房影像和超声波数据与临床元数据整合起来.
- 在5,025名确诊病理的患者的大型数据集 (19,360张来自5,216个乳房的图像) 上进行培训和测试.
- 使用前性收集的数据集进行验证 (来自187名患者的191个乳房).
主要成果:
- 多式模式模型在将瘤分类为良性或恶性方面,表现与经验丰富的放射科医生相提并论.
- 该模型在病理水平差异诊断方面表现优于放射科医生.
- 在前性验证中,该模型的准确性 (90.1%) 与活检样本的病理学家级评估 (92.7%) 相似.
结论:
- 多模式机器学习模型在协助瘤学家诊断乳腺癌方面显示出显著的潜力.
- 开发的模型为提高癌症诊断的准确性和效率提供了一个有希望的工具.
- 在瘤学工作流程中进一步整合人工智能可能会提高临床决策和患者的结果.
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