Using Deep Neural Network to Diagnose Thyroid Nodules on Ultrasound in Patients With Hashimoto's Thyroiditis.
Using Deep Neural Network to Diagnose Thyroid Nodules on Ultrasound in Patients With Hashimoto's Thyroiditis.
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DOI：
10.3389/fonc.2021.614172
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发表时间：
2021
影响因子：
4.7
通讯作者：
Zhan W
中科院分区：
文献类型：
--
作者：
Hou Y;Chen C;Zhang L;Zhou W;Lu Q;Jia X;Zhang J;Guo C;Qin Y;Zhu L;Zuo M;Xiao J;Huang L;Zhan W
本研究的目的是开发一种使用深度神经网络（DNN）的模型来诊断桥本甲状腺炎患者的甲状腺结节。在这项回顾性研究中，我们纳入了2017年1月至2019年8月在我院接受甲状腺超声检查的2,932名甲状腺结节患者。其中80%作为训练集，20%作为测试集。疑似恶性肿瘤的结节接受 FNA 或手术以获得病理结果。训练了两种 DNN 模型来诊断甲状腺结节，我们选择了性能更好的一种。模型将学习结节以及结节周围实质的特征，以在扩散的实质下获得更好的性能。使用10倍交叉验证和独立测试集来评估算法的性能。该模型的表现与三组临床经验分别为<5年、5-10年、>10年的放射科医生的表现进行了比较。总共从 2,932 名患者收集了 9,127 张图像，其中 7,301 张图像用于训练集，1,806 张图像用于测试集。 56% 的入组患者患有桥本甲状腺炎。该模型在测试集中区分恶性和良性结节的 AUC 为 0.924。它在弥漫性甲状腺实质和正常实质下表现出相似的性能，敏感性分别为 0.881 与 0.871 (p = 0.938)，特异性分别为 0.846 与 0.822 (p = 0.178)。在 HT 患者中，该模型区分恶性和良性结节的 AUC 为 0.924，显着高于三组放射科医生（AUC 分别 = 0.824、0.857、0.863，p < 0.05）。该模型在诊断正常和弥漫性实质下的甲状腺结节方面均表现出高性能。在桥本甲状腺炎患者中，与具有多年经验的放射科医生相比，该模型表现出更好的性能。
The aim of this study is to develop a model using Deep Neural Network (DNN) to diagnose thyroid nodules in patients with Hashimoto’s Thyroiditis. In this retrospective study, we included 2,932 patients with thyroid nodules who underwent thyroid ultrasonogram in our hospital from January 2017 to August 2019. 80% of them were included as training set and 20% as test set. Nodules suspected for malignancy underwent FNA or surgery for pathological results. Two DNN models were trained to diagnose thyroid nodules, and we chose the one with better performance. The features of nodules as well as parenchyma around nodules will be learned by the model to achieve better performance under diffused parenchyma. 10-fold cross-validation and an independent test set were used to evaluate the performance of the algorithm. The performance of the model was compared with that of the three groups of radiologists with clinical experience of <5 years, 5–10 years, >10 years respectively. In total, 9,127 images were collected from 2,932 patients with 7,301 images for the training set and 1,806 for the test set. 56% of the patients enrolled had Hashimoto’s Thyroiditis. The model achieved an AUC of 0.924 for distinguishing malignant and benign nodules in the test set. It showed similar performance under diffused thyroid parenchyma and normal parenchyma with sensitivity of 0.881 versus 0.871 (p = 0.938) and specificity of 0.846 versus 0.822 (p = 0.178). In patients with HT, the model achieved an AUC of 0.924 to differentiate malignant and benign nodules which was significantly higher than that of the three groups of radiologists (AUC = 0.824, 0.857, 0.863 respectively, p < 0.05). The model showed high performance in diagnosing thyroid nodules under both normal and diffused parenchyma. In patients with Hashimoto’s Thyroiditis, the model showed a better performance compared to radiologists with various years of experience.
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DOI：
10.1109/jbhi.2016.2636929
发表时间：
2017-01-01
影响因子：
7.7
作者：
Christodoulidis, Stergios;Anthimopoulos, Marios;Mougiakakou, Stavroula
通讯作者：
Mougiakakou, Stavroula
影响因子：
2.4
作者：
Huynh, Benjamin Q.;Li, Hui;Giger, Maryellen L.
通讯作者：
Giger, Maryellen L.
影响因子：
4.5
作者：
Tessler, Franklin N.;Middleton, William D.;Stavros, A. Thomas
通讯作者：
Stavros, A. Thomas
影响因子：
3.8
作者：
Park, Mina;Park, So Hee;Kwak, Jin Young
通讯作者：
Kwak, Jin Young
影响因子：
1.9
作者：
DELONG, ER;DELONG, DM;CLARKEPEARSON, DI
通讯作者：
CLARKEPEARSON, DI