Ultrasound image-based deep learning to assist in diagnosing gross extrathyroidal extension thyroid cancer: a retrospective multicenter study

Qi Qi; Xingzhi Huang; Yan Zhang; Shuangting Cai; Zhaoyou Liu; Taorong Qiu; Zihan Cui; Aiyun Zhou; Xinchun Yuan; Wan Zhu; Xiang Min; Yue Wu; Weijia Wang; Chunquan Zhang; Pan Xu

doi:10.1016/j.eclinm.2023.101905

Ultrasound image-based deep learning to assist in diagnosing gross extrathyroidal extension thyroid cancer: a retrospective multicenter study

EClinicalMedicine. 2023 Mar 24:58:101905. doi: 10.1016/j.eclinm.2023.101905. eCollection 2023 Apr.

Authors

Qi Qi¹, Xingzhi Huang¹, Yan Zhang², Shuangting Cai³, Zhaoyou Liu⁴, Taorong Qiu⁵, Zihan Cui⁵, Aiyun Zhou¹, Xinchun Yuan¹, Wan Zhu¹, Xiang Min⁶, Yue Wu¹, Weijia Wang⁷, Chunquan Zhang⁸, Pan Xu¹

Affiliations

¹ Department of Ultrasound, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
² Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, Shanghai, China.
³ Ultrasound Department, Jiujiang First People's Hospital, Jiujiang, Jiangxi, China.
⁴ Ultrasound Department, Affiliated Hospital of Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China.
⁵ School of Information Engineering, Nanchang University, Nanchang, Jiangxi, China.
⁶ Department of Otolaryngology, Head and Neck Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
⁷ Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
⁸ Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.

Abstract

Background: The presence of gross extrathyroidal extension (ETE) in thyroid cancer will affect the prognosis of patients, but imaging examination cannot provide a reliable diagnosis for it. This study was conducted to develop a deep learning (DL) model for localization and evaluation of thyroid cancer nodules in ultrasound images before surgery for the presence of gross ETE.

Methods: From January 2016 to December 2021 grayscale ultrasound images of 806 thyroid cancer nodules (4451 images) from 4 medical centers were retrospectively analyzed, including 517 no gross ETE nodules and 289 gross ETE nodules. 283 no gross ETE nodules and 158 gross ETE nodules were randomly selected from the internal dataset to form a training set and validation set (2914 images), and a multitask DL model was constructed for diagnosing gross ETE. In addition, the clinical model and the clinical and DL combined model were constructed. In the internal test set [974 images (139 no gross ETE nodules and 83 gross ETE nodules)] and the external test set [563 images (95 no gross ETE nodules and 48 gross ETE nodules)], the diagnostic performance of DL model was verified based on the pathological results. And then, compared the results with the diagnosis by 2 senior and 2 junior radiologists.

Findings: In the internal test set, DL model demonstrated the highest AUC (0.91; 95% CI: 0.87, 0.96), which was significantly higher than that of two senior radiologists [(AUC, 0.78; 95% CI: 0.71, 0.85; P < 0.001) and (AUC, 0.76; 95% CI: 0.70, 0.83; P < 0.001)] and two juniors radiologists [(AUC, 0.65; 95% CI: 0.58, 0.73; P < 0.001) and (AUC, 0.69; 95% CI: 0.62, 0.77; P < 0.001)]. DL model was significantly higher than clinical model [(AUC, 0.84; 95% CI: 0.79, 0.89; P = 0.019)], but there was no significant difference between DL model and clinical and DL combined model [(AUC, 0.94; 95% CI: 0.91, 0.97; P = 0.143)]. In the external test set, DL model also demonstrated the highest AUC (0.88, 95% CI: 0.81, 0.94), which was significantly higher than that of one of senior radiologists [(AUC, 0.75; 95% CI: 0.66, 0.84; P = 0.008) and (AUC, 0.81; 95% CI: 0.72, 0.89; P = 0.152)] and two junior radiologists [(AUC, 0.72; 95% CI: 0.62, 0.81; P = 0.002) and (AUC, 0.67; 95 CI: 0.57, 0.77; P < 0.001]. There was no significant difference between DL model and clinical model [(AUC, 0.85; 95% CI: 0.79, 0.91; P = 0.516)] and clinical + DL model [(AUC, 0.92; 95% CI: 0.87, 0.96; P = 0.093)]. Using DL model, the diagnostic ability of two junior radiologists was significantly improved.

Interpretation: The DL model based on ultrasound imaging is a simple and helpful tool for preoperative diagnosis of gross ETE thyroid cancer, and its diagnostic performance is equivalent to or even better than that of senior radiologists.

Funding: Jiangxi Provincial Natural Science Foundation (20224BAB216079), the Key Research and Development Program of Jiangxi Province (20181BBG70031), and the Interdisciplinary Innovation Fund of Natural Science, Nanchang University (9167-28220007-YB2110).

Keywords: Deep learning; Gross extrathyroidal extension thyroid cancer; Ultrasound.