Eskreis-Winkler Sarah, Onishi Natsuko, Pinker Katja, Reiner Jeffrey S, Kaplan Jennifer, Morris Elizabeth A, Sutton Elizabeth J
Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, NY.
University of California, Department of Radiology, San Francisco, CA.
J Breast Imaging. 2021 Mar 20;3(2):201-207. doi: 10.1093/jbi/wbaa102.
To investigate the feasibility of using deep learning to identify tumor-containing axial slices on breast MRI images.
This IRB-approved retrospective study included consecutive patients with operable invasive breast cancer undergoing pretreatment breast MRI between January 1, 2014, and December 31, 2017. Axial tumor-containing slices from the first postcontrast phase were extracted. Each axial image was subdivided into two subimages: one of the ipsilateral cancer-containing breast and one of the contralateral healthy breast. Cases were randomly divided into training, validation, and testing sets. A convolutional neural network was trained to classify subimages into "cancer" and "no cancer" categories. Accuracy, sensitivity, and specificity of the classification system were determined using pathology as the reference standard. A two-reader study was performed to measure the time savings of the deep learning algorithm using descriptive statistics.
Two hundred and seventy-three patients with unilateral breast cancer met study criteria. On the held-out test set, accuracy of the deep learning system for tumor detection was 92.8% (648/706; 95% confidence interval: 89.7%-93.8%). Sensitivity and specificity were 89.5% and 94.3%, respectively. Readers spent 3 to 45 seconds to scroll to the tumor-containing slices without use of the deep learning algorithm.
In breast MR exams containing breast cancer, deep learning can be used to identify the tumor-containing slices. This technology may be integrated into the picture archiving and communication system to bypass scrolling when viewing stacked images, which can be helpful during nonsystematic image viewing, such as during interdisciplinary tumor board meetings.
探讨使用深度学习识别乳腺MRI图像中包含肿瘤的轴位切片的可行性。
这项经机构审查委员会批准的回顾性研究纳入了2014年1月1日至2017年12月31日期间接受术前乳腺MRI检查的连续可手术浸润性乳腺癌患者。提取首次增强后阶段包含肿瘤的轴位切片。将每张轴位图像细分为两个子图像：一个是患侧含癌乳腺的子图像，另一个是对侧健康乳腺的子图像。病例被随机分为训练集、验证集和测试集。训练一个卷积神经网络将子图像分类为“有癌”和“无癌”类别。以病理作为参考标准，确定分类系统的准确性、敏感性和特异性。进行一项双阅片者研究，使用描述性统计方法测量深度学习算法节省的时间。
273例单侧乳腺癌患者符合研究标准。在保留测试集上，深度学习系统检测肿瘤的准确性为92.8%（648/706；95%置信区间：89.7%-93.8%）。敏感性和特异性分别为89.5%和94.3%。在不使用深度学习算法的情况下，阅片者花费3至45秒滚动到包含肿瘤的切片。
在包含乳腺癌的乳腺MR检查中，深度学习可用于识别包含肿瘤的切片。该技术可集成到图像存档与通信系统中，在查看堆叠图像时绕过滚动操作，这在非系统性图像查看（如跨学科肿瘤病例讨论会上）可能会有所帮助。
