Enhancing

Background: Methods: A total of 6,703 ultrasound images from 1,257 lesions in 1,202 patients with thyroid cancer were retrospectively collected. Since multiple ultrasound images were available for each lesion, the lesion-level prediction was derived as the average of the image-level outputs. Univariate and multivariate logistic regression were adopted to construct the clinical model. Six machine learning models were compared to identify the optimal one. A ResNet50-32x4d model was fine-tuned to build the DL model. The extreme gradient boosting (XGBoost) algorithm was employed to integrate the optimal radiomics score (radscore), DL scores, and clinical factors for combined model construction. The Shapley additive explanations (SHAP) algorithm and gradient-weighted class activation mapping technique were applied for interpretability. Results: Multivariate analysis identified the significant predictive variables to be sex [odds ratio (OR) =0.61; 95% confidence interval (CI): 0.54-0.69; P<0.001], age (OR =1.01; 95% CI: 1.00-1.01; P<0.001), tumor size (OR =0.54; 95% CI: 0.50-0.58; P<0.001), and multifocality (OR =0.66; 95% CI: 0.57-0.75; P<0.001). Among the six machine learning models, the XGBoost model demonstrated the best performance, with an area under the curve (AUC) of 0.809 and 0.745 in the training and test sets at the lesion level, respectively. The DL model outperformed the XGBoost model, achieving an AUC of 0.807 in the test set at the lesion level. The combined model exhibited comparable performance to that of the DL model, with AUCs of 0.845 and 0.814 in training and test sets at the lesion level, respectively. SHAP analysis revealed that DL scores and radscores were key contributors in predicting mutation status. Conclusions: The combined model integrating clinical and ultrasound data can effectively predict