Background Owing to the limited characterization of lymph nodes around the entrance point of the recurrent laryngeal nerve (LN-epRLN) in clinical lymph node negative (cN0) papillary thyroid carcinoma (PTC), this study sought to develop machine learning (ML) models to predict LN-epRLN metastasis, identify the optimal model, and improve interpretability using explainable artificial intelligence techniques. Methods We retrospectively reviewed 1,800 patients with cN0-PTC who underwent central lymph node dissection (CLND) with systematic LN-epRLN sampling. Histopathological evaluation confirmed metastatic status. Patients were randomly divided into training and testing sets at a 7:3 ratio. Nine ML models were constructed and optimized through 10-fold cross-validation and grid search. Performance was assessed using 11 metrics, including AUC, accuracy, sensitivity, and specificity. The best-performing model was compared against traditional nomograms via probability-based ranking analysis (PMRA). Results LN-epRLNs were identified in 149 out of 1800 PTC patients, with a metastasis rate of 19.46%. The Random Forest (RF) model outperformed others, achieving training/testing scores of 0.914/0.911 accuracy, 0.956/0.919 AUC, 0.993/0.974 specificity, and 0.609/0.500 sensitivity. A simplified model incorporating seven key predictors—total central lymph node metastasis number and ratio, pretracheal lymph node metastasis number and ratio, tumor size, age, and paratracheal lymph node metastasis number—retained high predictive performance. SHAPley Additive exPlanations (SHAP) analysis highlighted central compartment metastasis burden (number and ratio) as the most influential predictors. Conclusion The interpretable ML model developed in this study, leveraging the RF, provides a reliable tool for preoperative and intraoperative prediction of LN-epRLN metastasis in cN0 PTC patients. This approach has the potential to guide individualized surgical planning, optimizing the balance between oncological resection completeness and functional preservation.
Peng et al. (Mon,) studied this question.