A combined model integrating deep learning, radiomics, and clinical ultrasound features for predicting

Objective: This study aims to develop an integrated model that combines radiomics, deep learning features, and clinical and ultrasound characteristics for predicting Methods: This retrospective study included 717 thyroid nodules from 672 patients with PTC combined with HT from four hospitals in China. Deep learning and radiomics were employed to extract deep learning and radiomics features from ultrasound images. Feature selection was performed using Pearson's correlation coefficient, the Minimum Redundancy Maximum Relevance (mRMR) algorithm, and LASSO regression. The optimal algorithm was selected from nine machine learning algorithms for model construction, including the traditional radiomics model (RAD), the deep learning model (DL), and their fusion model (DL_RAD). Additionally, a final combined model was developed by integrating the DL_RAD model with clinical and ultrasound features. Model performance was assessed using AUC, calibration curves, and decision curve analysis (DCA), while SHAP analysis was used to interpret the contribution of each feature to the combined model's output. Results: The combined model achieved superior diagnostic performance, with AUC values of 0.895, 0.864, and 0.815 in the training, validation, and external test sets, respectively, outperforming the RAD model, DL model, and RAD_DL model. DeLong test results indicated significant differences in the external test set ( Conclusion: The combined model integrating radiomics, deep learning features, and clinical as well as ultrasound characteristics exhibits excellent diagnostic performance in predicting