AI diagnosis of Bethesda category IV thyroid nodules

Jincao Yao; Yanming Zhang; Jiafei Shen; Zhikai Lei; Jing Xiong; Bojian Feng; Xiaoxian Li; Wei Li; Di Ou; Yidan Lu; Na Feng; Meiying Yan; Jinjie Chen; Liyu Chen; Chen Yang; Liping Wang; Kai Wang; Jianhua Zhou; Ping Liang; Dong Xu

doi:10.1016/j.isci.2023.108114

AI diagnosis of Bethesda category IV thyroid nodules

iScience. 2023 Oct 4;26(11):108114. doi: 10.1016/j.isci.2023.108114. eCollection 2023 Nov 17.

Authors

Jincao Yao^{1

2

3

4}, Yanming Zhang^{5

6}, Jiafei Shen^{1

2}, Zhikai Lei⁷, Jing Xiong⁸, Bojian Feng^{1

2

9}, Xiaoxian Li¹⁰, Wei Li^{1

2}, Di Ou^{1

2}, Yidan Lu^{1

2}, Na Feng^{1

2}, Meiying Yan^{1

2}, Jinjie Chen¹¹, Liyu Chen^{1

2}, Chen Yang^{1

2}, Liping Wang^{1

2}, Kai Wang¹², Jianhua Zhou¹⁰, Ping Liang¹³, Dong Xu^{1

2

3

4

9}

Affiliations

¹ Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China.
² Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China.
³ Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou 310022, China.
⁴ Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou 310000, China.
⁵ Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou 310014, China.
⁶ Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou 310014, China.
⁷ Zhejiang University School of Medicine, Affiliated Hangzhou First People's Hospital, Hangzhou 310003, China.
⁸ Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, China.
⁹ Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital(Taizhou Cancer Hospital), Taizhou 317502, China.
¹⁰ Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
¹¹ Department of Statistical Science, Baylor University, Waco, TX 76706, USA.
¹² Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang 322100, China.
¹³ Department of Ultrasound, Chinese PLA General Hospital, Chinese PLA Medical School, Beijing 100853, China.

Abstract

Thyroid nodules are a common disease, and fine needle aspiration cytology (FNAC) is the primary method to assess their malignancy. For the diagnosis of follicular thyroid nodules, however, FNAC has limitations. FNAC can classify them only as Bethesda IV nodules, leaving their exact malignant status and pathological type undetermined. This imprecise diagnosis creates difficulties in selecting the follow-up treatment. In this retrospective study, we collected ultrasound (US) image data of Bethesda IV thyroid nodules from 2006 to 2022 from five hospitals. Then, US image-based artificial intelligence (AI) models were trained to identify the specific category of Bethesda IV thyroid nodules. We tested the models using two independent datasets, and the best AI model achieved an area under the curve (AUC) between 0.90 and 0.95, demonstrating its potential value for clinical application. Our research findings indicate that AI could change the diagnosis and management process of Bethesda IV thyroid nodules.

Keywords: cancer; medical imaging; neural networks.