Abstract
BACKGROUND: Seronegative Hashimoto's thyroiditis is often underdiagnosed due to the lack of antibody markers. Combining ultrasound radiomics with machine learning offers potential for early detection in patients with normal thyroid function. METHODS: Data from 164 patients with single thyroid lesions and normal thyroid function, treated surgically between 2016 and 2024, were retrospectively collected from four hospitals. Radiomics features were extracted from ultrasound images of non-tumorous hypoechoic areas. Pathological lymphocytic infiltration and hypoechoic ratios were evaluated by senior pathologists and ultrasound physicians. A machine learning model, CCH-NET, was developed using a random forest classifier after feature selection with Least Absolute Shrinkage and Selection Operator (LASSO) regression. The model was trained and tested with an 80:20 split and compared to senior ultrasound physicians. RESULTS: The CCH-NET model achieved a sensitivity of 0.762, specificity of 0.714, and an area under the curve (AUC) of 0.8248, outperforming senior ultrasound physicians (AUC = 0.681). It maintained consistent accuracy across test sets, with F1 scores of 0.778 and 0.720 in Test_1 and Test_2, respectively, and exhibited superior predictive rates. CONCLUSION: The CCH-NET model enhances accuracy in detecting early Seronegative Hashimoto's thyroiditis over senior ultrasound physicians. ETHICS: No. [2023] H013 TRIAL REGISTRATION: Chinese Clinical Trial Registry;CTR2400092179; 12 November 2024.