Risk factors for predicting lateral lymph node metastasis of papillary thyroid carcinoma based on LASSO-logistic regression.

Background: Many clinicians are facing the dilemma about whether therapeutic lateral lymph node dissection (LLND) should be applied to treat papillary thyroid carcinoma (PTC) patients with suspicious lateral lymph node metastasis (LLNM). This research plans to construct a model to predict the risk of LLNM in PTC patients. Methods: 389 PTC patients meeting the requirements were retrieved from the database of our hospital. The patients included were randomly divided into the training set (N1 = 244) and the validation set (N2 = 145). LASSO regression and logistic regression were used to screen the risk factors of LLNM. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to measure the performance of the predictive model. Results: In this study, a predictive model for LLNM in PTC patients was established based on LASSO and logistic regression models. Nomogram was established for visualization. The analyses of the area under the curve (AUC), calibration curve and decision curve of the training set and validation set all performed well, indicating that the prediction model has net benefit and clinical practicability. Conclusions: Nomogram based on LASSO regression can predict the risk of preoperative LLNM in PTC patients. This model can assist doctors in formulating individualized postoperative follow-up plans for PTC patients.