Deep Learning Based on ACR TI-RADS Can Improve the Differential Diagnosis of Thyroid Nodules - Wu, Ge-Ge; Lv, Wen-Zhi; Yin, Rui; Xu, Jian-Wei; Yan, Yu-Jing; Chen, Rui-Xue; Wang, Jia-Yu; Zhang, Bo; Cui, Xin-Wu; Dietrich, Christoph F. (Frontiers in Oncology, 2021)
Objective: The purpose of this study was to improve the differentiation between malignant and benign thyroid nodules using deep learning (DL) in category 4 and 5 based on the Thyroid Imaging Reporting and Data System (TI-RADS) from the American College of Radiology (ACR). Design and Methods: From June 2, 2017 to April 23, 2019, 2082 thyroid ultrasound images from 1396 consecutive patients with confirmed pathology were retrospectively collected, of which 1289 nodules were category 4 (TR4) and 793 nodules were category 5 (TR5). Ninety percent of the B-mode ultrasound images were applied for training and validation, and the residual 10% and an independent external dataset for testing purpose by three different deep learning algorithms. Results: In the independent test set, the DL algorithm of best performance got an AUC of 0.904, 0.845, 0.829 in TR4, TR5, and TR4&5, respectively. The sensitivity and specificity of the optimal model was 0.829, 0.831 on TR4, 0.846, 0.778 on TR5, 0.790, 0.779 on TR4&5, versus the radiologists of 0.686 (P=0.108), 0.766 (P=0.101), 0.677 (P=0.211), 0.750 (P=0.128), and 0.680 (P=0.023), 0.761 (P=0.530), respectively. Conclusions: The study demonstrated that DL based on ACR TI-RADS could improve the differentiation of malignant from benign thyroid nodules and had significant potential for clinical application on TR4 and TR5.