iScience (Nov 2023)
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
Affiliations
- Jincao Yao
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China; Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou 310022, China; Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou 310000, China
- Yanming Zhang
- Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou 310014, China; Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou 310014, China
- Jiafei Shen
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
- Zhikai Lei
- Zhejiang University School of Medicine, Affiliated Hangzhou First People’s Hospital, Hangzhou 310003, China
- Jing Xiong
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, China
- Bojian Feng
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China; Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital(Taizhou Cancer Hospital), Taizhou 317502, China
- Xiaoxian Li
- 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
- Wei Li
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
- Di Ou
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
- Yidan Lu
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
- Na Feng
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
- Meiying Yan
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
- Jinjie Chen
- Department of Statistical Science, Baylor University, Waco, TX 76706, USA
- Liyu Chen
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
- Chen Yang
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
- Liping Wang
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
- Kai Wang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang 322100, China
- Jianhua Zhou
- 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; Corresponding author
- Ping Liang
- Department of Ultrasound, Chinese PLA General Hospital, Chinese PLA Medical School, Beijing 100853, China; Corresponding author
- Dong Xu
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China; Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou 310022, China; Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou 310000, China; Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital(Taizhou Cancer Hospital), Taizhou 317502, China; Corresponding author
- Journal volume & issue
-
Vol. 26,
no. 11
p. 108114
Abstract
Summary: 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.
