Artificial intelligence-enabled ultrasound diagnosis and stratification of follicular thyroid neoplasms: a multi-center study.
Authors
Affiliations (29)
Affiliations (29)
- Department of Interventional Ultrasound, Senior Department of Oncology, Chinese PLA General Hospital, Beijing, China.
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
- Department of Ultrasound, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, China.
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
- Department of Ultrasound, Peking University Third Hospital, Beijing, China.
- Department of Ultrasound, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China.
- Department of Ultrasound, Affiliated Hospital of Jiangsu University, Zhenjiang, China.
- Department of Ultrasound, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
- Department of Ultrasound, Shanxi Cancer Hospital, Taiyuan, China.
- Department of Medical Ultrasound, Peking University Shenzhen Hospital, Shenzhen, China.
- Department of Interventional Ultrasound, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
- Department of Ultrasound, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
- Department of ultrasound medicine, The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, Huaian, China.
- Department of Ultrasound, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangdong, Guangzhou, China.
- Department of Interventional Ultrasound, Qinghai Provincial People's Hospital, Xining, China.
- Department of Thyroid, Handan Hangang Hospital, Handan City, Hebei, China.
- Department of Ultrasound, The First Affiliated Hospital of China Medical University, Shenyang, China.
- Department of Ultrasound Medicine, Beijing Hospital, Beijing, China.
- Department of Medical Ultrasound, Xuzhou Central Hospital, Xuzhou, China.
- Department of Interventional Ultrasound, The Second Hospital of Anhui Medical University, Hefei, China.
- Department of Interventional Ultrasound, Puyang Traditional Chinese Medicine Hospital, Puyang, Henan, China.
- Department of Pathology, Peking University Third Hospital, Beijing, China.
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Interventional Ultrasound, Senior Department of Oncology, Chinese PLA General Hospital, Beijing, China. [email protected].
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. [email protected].
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China. [email protected].
- Department of Interventional Ultrasound, Senior Department of Oncology, Chinese PLA General Hospital, Beijing, China. [email protected].
Abstract
Preoperatively distinguishing follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA) remains a significant clinical challenge. Current ultrasound risk stratification systems show limited efficacy for follicular neoplasms, and existing artificial intelligence (AI) approaches lack sufficient validation. We developed and validated a deep learning model using ultrasound images to differentiate FTC from FTA and classify FTC into invasion subtypes. This multicenter retrospective study incorporated data from 31 hospitals, using 1531 patients for model development and 900 across three external test sets for validation. The model demonstrated high diagnostic performance, with AUCs of 0.816-0.847 for FTC vs FTA discrimination across external test sets and robust performance across subtypes (AUC range 0.754-0.910), and generalized well to varied clinical settings. Triple-classification macro-AUCs were 0.818-0.861. It consistently outperformed radiologists and improved diagnostic accuracy as an assistive tool. Our AI model provides a reliable, non-invasive tool for preoperative diagnosis and risk stratification of follicular thyroid neoplasms.