The value of machine learning models in predicting lung metastasis in papillary thyroid carcinoma.

Objective: To construct a machine learning (ML) model to predict the risk of lung metastasis in papillary thyroid carcinoma (PTC). Methods: This retrospective study included 681 PTC patients who received radioactive iodine (RAI) treatment for the first time. Among them, 71 patients were diagnosed with lung metastasis (10.4%). The patients' age, sex, stimulated thyroglobulin (s-Tg) level ahead of Results: The selected features in the final model included "maximum tumor diameter", "number of invading tumors", and "s-Tg". The RF model had the highest area under the curve (AUC) of 0.935, whereas the SVM had the lowest generalization ability (AUC = 0.863). In the testing of all models, LR achieved an AUC of 0.928. Although the RF model had a slightly higher AUC of 0.935, the LR model was ultimately chosen due to its superior generalization ability as indicated by its highest performance in AUPRC of 0.74 in cross-validation. DCA showed that under most threshold probabilities, the net benefit of the LR model exceeded other models. A nomogram was used to display the LR results. Conclusion: LASSO regression analysis identified that large primary tumor, high number of invading tumors, high s-Tg level ahead of