Machine learning-based prediction of lymph node metastasis in papillary thyroid carcinoma: an interpretable multidimensional model.

Background: Lymph node metastasis (LNM) is a prevalent feature in papillary thyroid carcinoma (PTC) that significantly influences surgical management and prognosis. Accurate identification of LNM risk is essential for optimizing adjuvant therapy and long-term surveillance strategies in clinical practice. Methods: This retrospective study analyzed 232 patients who underwent thyroidectomy. We employed LASSO regression to select six predictive features from a comprehensive covariate panel. Subsequently, seven machine learning algorithms were trained and evaluated via grid search and 5-fold cross-validation. The optimal model was interpreted using SHAP analysis to elucidate feature contributions. Results: LASSO-based feature selection retained six predictive features: Conclusion: The SVM-based model demonstrated favorable internal validation performance and provided an interpretable framework for individualized LNM risk stratification in patients with PTC. Further prospective multicenter validation is required before routine clinical implementation.