Classification of breast masses on ultrasound shear wave elastography using convolutional neural networks
Classification of breast masses on ultrasound shear wave elastography using convolutional neural networks
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DOI：
10.1177/0161734620932609
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发表时间：
2020-06-05
影响因子：
2.3
通讯作者：
Tateishi, Ukihide
中科院分区：
文献类型：
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作者：
Fujioka, Tomoyuki;Katsuta, Leona;Tateishi, Ukihide
我们的目的是利用深度学习和卷积神经网络(CNN)来区分超声剪切波弹性成像(SWE)上的乳腺良恶性肿块图像。我们回顾收集了158幅良性肿块图像和146幅恶性肿块图像作为SWE的训练数据。深度学习模型是使用几种CNN架构(Xception、InceptionV3、InceptionResNetV2、DenseNet121、DenseNet169和NASNetMobile)构建的，具有50、100和200个纪元。分析38个良性肿块和35个恶性肿块的SWE图像作为检验数据。两位放射科医生通过使用5点视觉颜色评估(SWEc)和平均弹性值(以kpa为单位)(Swee)的共识读数来解释这些测试数据。计算灵敏度、特异度和受试者工作特征曲线下面积(AUC)。最好的美国有线电视新闻网模型(DenseNet169,100个时代)、SWEc和Swee的敏感性分别为0.857、0.829和0.914，特异性分别为0.789、0.737和0.763。CNN的平均AUC值为0.870(范围为0.844-0.898)，SWEc和Swee的AUC值分别为0.821和0.855。与放射科医生的读数相比，CNN的诊断性能相同或更好。与SWEc相比，DenseNet169(100个时期)、XERCEL(50个时期)和XERCEL(100个时期)的诊断性能更好(P=0.018-0.037)。在超声SWE上，与放射科医生相比，深度学习CNN显示出与放射科医生相同或更高的AUC，以区分乳腺良恶性肿块。
We aimed to use deep learning with convolutional neural networks (CNNs) to discriminate images of benign and malignant breast masses on ultrasound shear wave elastography (SWE). We retrospectively gathered 158 images of benign masses and 146 images of malignant masses as training data for SWE. A deep learning model was constructed using several CNN architectures (Xception, InceptionV3, InceptionResNetV2, DenseNet121, DenseNet169, and NASNetMobile) with 50, 100, and 200 epochs. We analyzed SWE images of 38 benign masses and 35 malignant masses as test data. Two radiologists interpreted these test data through a consensus reading using a 5-point visual color assessment (SWEc) and the mean elasticity value (in kPa) (SWEe). Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. The best CNN model (which was DenseNet169 with 100 epochs), SWEc, and SWEe had a sensitivity of 0.857, 0.829, and 0.914 and a specificity of 0.789, 0.737, and 0.763 respectively. The CNNs exhibited a mean AUC of 0.870 (range, 0.844-0.898), and SWEc and SWEe had an AUC of 0.821 and 0.855. The CNNs had an equal or better diagnostic performance compared with radiologist readings. DenseNet169 with 100 epochs, Xception with 50 epochs, and Xception with 100 epochs had a better diagnostic performance compared with SWEc (P = 0.018-0.037). Deep learning with CNNs exhibited equal or higher AUC compared with radiologists when discriminating benign from malignant breast masses on ultrasound SWE.