Radiomics-Based machine learning for preoperative prediction of Large-Number central lymph node metastasis in papillary thyroid carcinoma.

PURPOSE: This study aimed to develop interpretable machine learning (ML) models integrating preoperative clinical, ultrasound (US), and radiomic features for predicting large-number central lymph node metastasis (CLNM) in papillary thyroid carcinoma (PTC). METHODS: This retrospective study analyzed 1,400 PTC patients undergoing thyroidectomy with central lymph node dissection (CLND) between January 2018 and May 2025. Stratified by large-number CLNM status, patients were randomized into training (n = 980) and test (n = 420) cohorts. Key predictors were identified via Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression. Six ML algorithms were trained using Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance. Performance evaluation used the area under the receiver operating characteristic curve (ROC-AUC), precision-recall curve AUC (PR-AUC), and decision curve analysis (DCA). Optimal model interpretation used Shapley Additive Explanations (SHAP). RESULTS: Large-number CLNM occurred in 92 (6.6%) patients. Predictors included clinical/US features (age, tumor size, suspicious nodes, tracheal capsular invasion) and a 7-feature LASSO-selected radiomics signature. The integrated model achieved ROC-AUCs of 0.880 (95% confidence interval: 0.841–0.912; training) and 0.827 (0.738–0.901; testing), significantly outperforming submodels in PR-AUC (p < 0.05). SHAP quantified feature contributions. CONCLUSION: This ML model preoperatively identifies PTC patients at high risk for large-number CLNM, offering potential to personalize treatment decisions and reduce unnecessary surgery.