乳房扫描图像的深度学习,以减少不必要的乳房活检:一项初步研究
Chang Liu1, Min Sun2, Dooman Arefan3
1Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
Breast cancer research : BCR
|May 24, 2024
概括
一个深度学习算法可以分析乳房造影以识别乳房成像报告和数据系统 (BI-RADS) 4可能不需要活检的病变. 这种方法旨在减少不必要的侵入性手术和相关的患者压力.
科学领域:
- 放射学和医学成像学 医学成像学
- 人工智能在医学中的应用
- 在瘤学瘤学.
背景情况:
- 乳腺成像报告和数据系统 (BI-RADS) 4病变经常导致活检,70%-80%的结果是良性的.
- 目前的做法需要对BI-RADS 4发现进行活检,这导致了不必要的侵入性手术的高率.
研究的目的:
- 开发和评估BI-RADS 4乳房镜病变的深度学习分类算法.
- 通过准确分类可疑病变,减少不必要的乳腺活检.
主要方法:
- 一项对847名BI-RADS 4病变患者的回顾性研究.
- 在四个二进制分类任务中利用卷积神经网络.
- 分类包括良性与恶性,和非典型与恶性.
主要成果:
- 该算法在所有任务中实现了95%的灵敏度来检测更高阶段的疾病.
- 具体性在25%至46%之间,取决于分类任务.
- 曲线下的面积 (AUC) 值在0.67至0.73.7之间.
结论:
- 通过对乳房影像进行深度学习分析,可以识别可能不需要活检的BI-RADS 4病变.
- 这项技术有可能减少不必要的程序,成本和患者的焦虑.
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