Table 2_Machine learning-based prediction of lymph node metastasis in papillary thyroid carcinoma: an interpretable multidimensional model.docx

Accurate identification of LNM risk is essential for optimizing adjuvant therapy and long-term surveillance strategies in clinical practice.</p>Methods<p>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.</p>Results<p>LASSO-based feature selection retained six predictive features: BRAF mutation status, tumor size, capsular invasion, extrathyroidal extension, multifocality, and TSH. Among the seven algorithms, the Support Vector Machine (SVM) model showed the most favorable validation performance, achieving an AUC of 0.849 (95% CI, 0.756–0.934) and an accuracy of 0.768 in the validation cohort.