定量超声与机器学习在 BI-RADS 4-5 级实性乳腺病灶评估中的附加价值。
Added Value of Quantitative Ultrasound and Machine Learning in BI-RADS 4-5 Assessment of Solid Breast Lesions.
机构信息
Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center (CRCHUM), Montréal, Québec, Canada.
Department of Radiology, Breast Imaging Center, University of Montreal Hospital (CHUM), Montréal, Québec, Canada; Department of Radiology, Radio-Oncology and Nuclear Medicine, University of Montreal, Montréal, Québec, Canada.
出版信息
Ultrasound Med Biol. 2020 Feb;46(2):436-444. doi: 10.1016/j.ultrasmedbio.2019.10.024. Epub 2019 Nov 27.
The purpose of this study was to evaluate various combinations of 13 features based on shear wave elasticity (SWE), statistical and spectral backscatter properties of tissues, along with the Breast Imaging Reporting and Data System (BI-RADS), for classification of solid breast lesions at ultrasonography by means of random forests. One hundred and three women with 103 suspicious solid breast lesions (BI-RADS categories 4-5) were enrolled. Before biopsy, additional SWE images and a cine sequence of ultrasound images were obtained. The contours of lesions were delineated, and parametric maps of the homodyned-K distribution were computed on three regions: intra-tumoral, supra-tumoral and infra-tumoral zones. Maximum elasticity and total attenuation coefficient were also extracted. Random forests yielded receiver operating characteristic (ROC) curves for various combinations of features. Adding BI-RADS category improved the classification performance of other features. The best result was an area under the ROC curve of 0.97, with 75.9% specificity at 98% sensitivity.
本研究旨在通过随机森林，评估基于剪切波弹性（SWE）、组织统计和背散射特性以及乳腺影像报告和数据系统（BI-RADS）的 13 种特征的各种组合，对超声检查中可疑实性乳腺病变进行分类。共纳入 103 名女性，共 103 个可疑实性乳腺病变（BI-RADS 类别 4-5）。在活检前，获得了额外的 SWE 图像和超声图像的电影序列。描绘了病变的轮廓，并在三个区域：肿瘤内、肿瘤上和肿瘤下区域计算了同源 K 分布的参数图。还提取了最大弹性和总衰减系数。随机森林为各种特征组合生成了接收者操作特征（ROC）曲线。添加 BI-RADS 类别提高了其他特征的分类性能。最佳结果是 ROC 曲线下面积为 0.97，特异性为 75.9%，敏感性为 98%。
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