Deep learning model for breast cancer diagnosis based on bilateral asymmetrical detection (BilAD) in digital breast tomosynthesis images
Deep learning model for breast cancer diagnosis based on bilateral asymmetrical detection (BilAD) in digital breast tomosynthesis images
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DOI：
10.1007/s12194-022-00686-y
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发表时间：
2022-11-07
影响因子：
1.6
通讯作者：
Ueda, Takuya
中科院分区：
文献类型：
--
作者：
Shimokawa, Daiki;Takahashi, Kengo;Ueda, Takuya
本研究的目的是开发一个深度学习模型，通过嵌入检查双侧乳房组织不对称的诊断算法来诊断乳腺癌。这项回顾性研究得到了机构审查委员会的批准。共有115名接受过乳房手术并经病理证实患有乳腺癌的患者参加了这项研究。本研究对每位患者生成两对图像[230对双侧乳腺数字乳房断层合成（DBT）图像，其中115个恶性肿瘤和对侧组织（M/N）， 115个双侧正常区域（N/N）]。所提出的深度学习模型被称为双侧不对称检测（BilAD），它是一种改进的卷积神经网络（CNN）模型，用于双侧乳房图像的二维张量。训练BilAD对M/N和N/N数据集对之间的差异进行分类。将BilAD模型的结果与单侧控制CNN模型（uCNN）的结果进行比较。BilAD和uCNN的结果如下：准确率分别为0.84和0.75；灵敏度分别为0.73和0.58；特异性分别为0.93和0.92。BilAD的受试者工作特征曲线下平均面积显著高于uCNN (p = 0.02)：分别为0.90和0.84。本文提出的深度学习模型通过嵌入诊断算法来检测双侧乳腺组织的不对称性，从而提高了乳腺癌的诊断准确性。
The purpose of this study was to develop a deep learning model to diagnose breast cancer by embedding a diagnostic algorithm that examines the asymmetry of bilateral breast tissue. This retrospective study was approved by the institutional review board. A total of 115 patients who underwent breast surgery and had pathologically confirmed breast cancer were enrolled in this study. Two image pairs [230 pairs of bilateral breast digital breast tomosynthesis (DBT) images with 115 malignant tumors and contralateral tissue (M/N), and 115 bilateral normal areas (N/N)] were generated from each patient enrolled in this study. The proposed deep learning model is called bilateral asymmetrical detection (BilAD), which is a modified convolutional neural network (CNN) model of Xception with two-dimensional tensors for bilateral breast images. BilAD was trained to classify the differences between pairs of M/N and N/N datasets. The results of the BilAD model were compared to those of the unilateral control CNN model (uCNN). The results of BilAD and the uCNN were as follows: accuracy, 0.84 and 0.75; sensitivity, 0.73 and 0.58; and specificity, 0.93 and 0.92, respectively. The mean area under the receiver operating characteristic curve of BilAD was significantly higher than that of the uCNN (p = 0.02): 0.90 and 0.84, respectively. The proposed deep learning model trained by embedding a diagnostic algorithm to examine the asymmetry of bilateral breast tissue improves the diagnostic accuracy for breast cancer.