Development of Radiomics-Based Risk Prediction Models for Stages of Hashimoto’s Thyroiditis Using Ultrasound, Clinical, and Laboratory Factors

作者：Jiahui Chen, Kai Kang, Xinyue Wang, Jianning Chi, Xuemeng Gao, Yongxin Li, Ying Huang · 发表于：Ultrasound in Medicine & Biology · 年份：2025 · DOI：10.1016/j.ultrasmedbio.2025.05.025 · 被引用次数：2 · 研究领域：Thyroid Cancer Diagnosis and Treatment、Thyroid Disorders and Treatments、Radiomics and Machine Learning in Medical Imaging

OBJECTIVES: To develop a radiomics risk-predictive model for differentiating the different stages of Hashimoto's thyroiditis (HT). METHODS: Data from patients with HT who underwent definitive surgical pathology between January 2018 and December 2023 were retrospectively collected and categorized into early HT (HT patients with simple positive antibodies or simultaneously accompanied by elevated thyroid hormones) and late HT (HT patients with positive antibodies and beginning to present subclinical hypothyroidism or developing hypothyroidism). Ultrasound images and five clinical and 12 laboratory indicators were obtained. Six classifiers were used to construct radiomics models. The gradient boosting decision tree (GBDT) classifier was used to screen for the best features to explore the main risk factors for differentiating early HT. The performance of each model was evaluated by receiver operating characteristic (ROC) curve. The model was validated using one internal and two external test cohorts. RESULTS: A total of 785 patients were enrolled. Extreme gradient boosting (XGBOOST) showed best performance in the training cohort, with an AUC of 0.999 (0.998, 1), and AUC values of 0.993 (0.98, 1), 0.947 (0.866, 1), and 0.98 (0.939, 1), respectively, in the internal test, first external, and second external cohorts. Ultrasound radiomic features contributed to 78.6% (11/14) of the model. The first-order feature of traverse section of thyroid ultrasound image, texture feature gray-le...