Machine learning-based model for predicting contralateral central lymph node metastasis in papillary thyroid carcinoma with isthmus proximity.

Background: Papillary thyroid carcinoma (PTC) originating from the isthmus exhibits a marked tendency for contralateral central lymph nodes (Cont-CLNs) metastasis. To accurately assess this risk, this study aims to establish and validate an individualized predictive model for contralateral central zone lymph node metastasis in PTC with isthmus proximity using machine learning algorithms. Methods: This retrospective study analyzed 1,672 patients with PTC. Based on tumor location, patients were categorized into a group with PTC with isthmus proximity and a non-isthmic group to compare the incidence of Cont-CLNs metastasis. Subsequently, we focused on 397 patients with PTC with isthmus proximity, who were randomly allocated in a 7:3 ratio to a training set (n=279) and a validation set (n=118). Feature selection was performed using the Boruta algorithm and LASSO regression. Seven machine learning algorithms were then employed to construct prediction models. Model performance was evaluated using metrics including the AUC, sensitivity, and specificity. The optimal model was interpreted using the shapley additive explanations (SHAP) method. Results: This study included 1,672 patients with PTC. The rate of Cont-CLNs metastasis was significantly higher in patients with unilateral PTC with isthmus proximity (n=397) than in those with non-isthmic PTC (33% vs. 12%, Conclusion: The machine learning model developed in this study effectively predicts the risk of Cont-CLNs metastasis in patients with unilateral PTC with isthmus proximity, providing a valuable tool to support personalized surgical decision-making regarding the extent of lymph node dissection.