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Interpretable Machine Learning to Predict the Malignancy Risk of Follicular Thyroid Neoplasms in Extremely Unbalanced Data: Retrospective Cohort Study and Literature Review

Interpretable Machine Learning to Predict the Malignancy Risk of Follicular Thyroid Neoplasms in Extremely Unbalanced Data: Retrospective Cohort Study and Literature Review

We evaluated the Thyroid Imaging Reporting and Data System for Follicular Neoplasm (F-TIRADS) scoring criteria developed by Li et al [18] to differentiate between benign and malignant cases in our dataset. These criteria are based on 6 key features: mean diameter, composition, echogenicity, margin, calcifications, and trabecular formation. Each specific characteristic of these features is assigned a corresponding point value, and the total points across the 6 features indicate the risk level of FTC.

Rui Shan, Xin Li, Jing Chen, Zheng Chen, Yuan-Jia Cheng, Bo Han, Run-Ze Hu, Jiu-Ping Huang, Gui-Lan Kong, Hui Liu, Fang Mei, Shi-Bing Song, Bang-Kai Sun, Hui Tian, Yang Wang, Wu-Cai Xiao, Xiang-Yun Yao, Jing-Ming Ye, Bo Yu, Chun-Hui Yuan, Fan Zhang, Zheng Liu

JMIR Cancer 2025;11:e66269