To develop a preoperative predictive model for central compartment lymph node metastasis (CLNM) in papillary thyroid carcinoma (PTC) by integrating ultrasound radiomics features, deep learning features, and clinical characteristics and to evaluate its performance.

A retrospective analysis was conducted on 510 pathologically confirmed PTC patients who underwent surgical treatment at Fudan University Shanghai Cancer Center between December 2020 and March 2023. All patients received ultrasound-guided fine-needle aspiration biopsy preoperatively. Postoperative pathology identified 387 CLNM-negative and 123 CLNM-positive cases. Regions of interest were manually delineated on grayscale ultrasound images by experienced radiologists. Radiomics features were extracted using Pyradiomics, and deep learning features were obtained via a pretrained VGG16-BN model. After feature selection and dimensionality reduction, the final radiomics and deep learning features were combined with clinical features to construct a multimodal feature set. All cases were split into training and independent test sets at a 4∶1 ratio. Five-fold cross-validation was applied during training for model construction and hyperparameter optimization, and a support vector machine (SVM)-based predictive model was established. Model performance was evaluated on the independent test set using the area under the receiver operating characteristic curve (AUC) and its 95% confidence interval (CI).

ROC curve analysis showed that the discriminative ability of the radiomics-only model was the weakest (AUC=0.63), while the performance of the deep learning-only model was significantly improved (AUC=0.91). The bimodal fusion model combining deep learning and radiomics further enhanced the performance (AUC=0.92), and the full-feature fusion model (radiomics + deep learning + clinical features) achieved the best cross-validation performance (AUC=0.93). On the independent test set, the trimodal fusion model achieved an AUC of 0.962 (95%CI: 0.915-0.994), a specificity of 0.987 (95%CI: 0.931-0.999), and a sensitivity of 0.583 (95%CI: 0.366-0.779), demonstrating strong overall discriminative ability and high negative exclusion capability.

The multimodal predictive model integrating ultrasound radiomics, deep learning features, and clinical risk factors effectively enhances preoperative risk stratification for CLNM in PTC patients, offering potential clinical decision-support value. Its generalizability warrants further validation in multicenter, large-sample studies.