Diagnosis of thyroid nodules on ultrasonography by a deep convolutional neural network
Diagnosis of thyroid nodules on ultrasonography by a deep convolutional neural network
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DOI：
10.1038/s41598-020-72270-6
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发表时间：
2020-09-17
影响因子：
4.6
通讯作者：
Kwak, Jin Young
中科院分区：
文献类型：
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作者：
Koh, Jieun;Lee, Eunjung;Kwak, Jin Young
本研究的目的是评价和比较深度卷积神经网络（CNN）和放射科专家在超声检查（US）上区分甲状腺结节的诊断性能，并在多中心数据集中验证结果。这项多中心回顾性研究收集了15，375张甲状腺结节的US图像，用于算法开发（n= 13，560，Severance医院，SH训练集）、内部测试（n=634，SH测试集）和外部测试（n=781，Samsung Medical Center，SMC集; n=200，CHA Bundang Medical Center，CBMC集; n=200，Kyung Hee University Hospital，KUH集）。测试了两个单独的CNN和两个分类集合（CNNE 1和CNNE 2）来区分恶性和良性甲状腺结节。CNN显示出诊断恶性甲状腺结节的高曲线下面积（AUC）（内部测试集为0.898-0.937，外部测试集为0.821-0.885）。在SH测试集中，CNNE 2的AUC显著高于放射科医师（0.932 vs. 0.840，P
The purpose of this study was to evaluate and compare the diagnostic performances of the deep convolutional neural network (CNN) and expert radiologists for differentiating thyroid nodules on ultrasonography (US), and to validate the results in multicenter data sets. This multicenter retrospective study collected 15,375 US images of thyroid nodules for algorithm development (n=13,560, Severance Hospital, SH training set), the internal test (n=634, SH test set), and the external test (n=781, Samsung Medical Center, SMC set; n=200, CHA Bundang Medical Center, CBMC set; n=200, Kyung Hee University Hospital, KUH set). Two individual CNNs and two classification ensembles (CNNE1 and CNNE2) were tested to differentiate malignant and benign thyroid nodules. CNNs demonstrated high area under the curves (AUCs) to diagnose malignant thyroid nodules (0.898-0.937 for the internal test set and 0.821-0.885 for the external test sets). AUC was significantly higher for CNNE2 than radiologists in the SH test set (0.932 vs. 0.840, P