AI diagnosis of Bethesda category IV thyroid nodules

• Published in: iScience Vol. 26; no. 11; p. 108114
• Main Authors: Yao, Jincao, Zhang, Yanming, Shen, Jiafei, Lei, Zhikai, Xiong, Jing, Feng, Bojian, Li, Xiaoxian, Li, Wei, Ou, Di, Lu, Yidan, Feng, Na, Yan, Meiying, Chen, Jinjie, Chen, Liyu, Yang, Chen, Wang, Liping, Wang, Kai, Zhou, Jianhua, Liang, Ping, Xu, Dong
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 17.11.2023; Elsevier
• Subjects: cancer; medical imaging; neural networks; cancer; medical imaging; neural networks
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2023.108114

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. [Display omitted] •Individuals with Bethesda category IV thyroid nodules are subtyped using an AI model•AI-based method can serve as a supplement to fine needle aspiration cytology (FNAC)•The management of Bethesda IV thyroid nodules could be changed by AI technology Medical imaging; Neural networks; Cancer