优化S-detect对BI-RADS 4乳腺结节的分类精度,使用多模式超声波参数
Jinli Wang1, Hui Ma2, Sirui Wang3
1Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China.
Quantitative imaging in medicine and surgery
|February 11, 2026
概括
将多式超声波 (MUS) 参数与S-detect深度学习工具集成,显著提高了乳腺结节分类的准确性. 这种综合方法提高了BI-RADS 4病变的诊断特异性,有助于临床决策.
科学领域:
- 医学成像分析分析 医学成像分析
- 人工智能在诊断中的应用
- 乳腺癌检测 乳腺癌检测
背景情况:
- 深度学习 (DL) 工具S-detect显示乳腺成像报告和数据系统 (BI-RADS 4) 乳腺病变的诊断特异性有限 (59.57%).
- 定量多式超声波 (MUS) 参数提高S-detect性能的潜力尚未得到充分证实.
研究的目的:
- 提高S-detect的诊断准确度,以区分良性与恶性乳腺结节.
- 调查将MUS参数集成到S-detect工具中的有效性.
主要方法:
- 对231名BI-RADS 4乳腺结节的女性患者的临床和超声数据的回顾性分析.
- 提取和分析定量MUS参数 (例如,血管阻力指数,化,弹性应变比率,血管性指数).
- 使用MUS参数优化之前和之后评估S-detect分类性能,使用灵敏度,特异性,精度和AUC.
主要成果:
- 与良性结节相比,恶性结节的弹性应变比率 (SR) 和血管性指数 (VI) 显著提高.
- 多变量分析确定了SR,VI,血管抵抗指数,化和特定轴平面特征作为恶性瘤的独立预测因素.
- 结合的S-detect+MUS模型实现了高诊断性能 (SE 86.75%,SP 92.31%,AUC 0.93),显著提高了BI-RADS 4a病变的特异性.
结论:
- 将S-detect与MUS参数结合起来,可以大大提高BI-RADS病变差异诊断的准确性.
- 这种综合方法为乳腺结节评估的临床决策提供了可靠的基础.
- 建议进行更多的多中心前性研究来验证这些发现.
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