Li Shuying, Zhang Menghao, Xue Minghao, Zhu Quing
Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA.
Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, USA.
J Biophotonics. 2024 May;17(5):e202300483. doi: 10.1002/jbio.202300483. Epub 2024 Mar 2.
Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated potential for breast cancer diagnosis, in which real-time or near real-time diagnosis with high accuracy is desired. However, DOT's relatively slow data processing and image reconstruction speeds have hindered real-time diagnosis. Here, we propose a real-time classification scheme that combines US breast imaging reporting and data system (BI-RADS) readings and DOT frequency domain measurements. A convolutional neural network is trained to generate malignancy probability scores from DOT measurements. Subsequently, these scores are integrated with BI-RADS assessments using a support vector machine classifier, which then provides the final diagnostic output. An area under the receiver operating characteristic curve of 0.978 is achieved in distinguishing between benign and malignant breast lesions in patient data without image reconstruction.
超声（US）引导下的漫射光学断层扫描（DOT）已显示出在乳腺癌诊断方面的潜力，其中需要进行高精度的实时或近实时诊断。然而，DOT相对较慢的数据处理和图像重建速度阻碍了实时诊断。在此，我们提出一种实时分类方案，该方案结合了美国乳腺影像报告和数据系统（BI-RADS）读数以及DOT频域测量。训练一个卷积神经网络以从DOT测量中生成恶性概率分数。随后，使用支持向量机分类器将这些分数与BI-RADS评估相结合，然后提供最终的诊断输出。在无需图像重建的患者数据中，区分良性和恶性乳腺病变时，受试者操作特征曲线下面积达到0.978。
