Analysis of Risk Factors and Risk Prediction for Cervical Lymph Node Metastasis in Thyroid Papillary Carcinoma.

Background: To analyze the risk factors of cervical lymph node metastasis (LNM) of thyroid papillary carcinoma (PTC) and construct the prediction model.

Methods: Clinical data of 1105 patients with pathologically confirmed PTC in our hospital from February 2019 to May 2024 were retrospectively analyzed, and randomly divided into a training set and validation set according to the proportion of 7:3. With cervical central LNM (CLNM) and lateral LNM (LLNM) as outcome variables respectively, ultrasound characteristics were analyzed and C-TIRADS scores were performed Combined with the general situation of the patient, preoperative serum thyroglobulin (Tg) level, BRAFV600E (hereinafter referred to as BRAF) gene mutation and other characteristics of the patient, analysis was conducted to determine the independent risk factors for cervical CLNM and LLNM of PTC, and establish Nomogram prediction models. The test data set is used to validate the model. The area under the ROC curve (AUC) and the decision curve analysis (DCA) were used to evaluate the prediction efficiency of the model.

Results: The analysis shows that male, age < 55 years old, tumor diameter ≥ 1 cm, capsular invasion, positive serum thyroglobulin (Tg), BRAF gene mutation type and C-TIRADS score are independent risk factors for cervical CLNM in PTC (P < 0.05). Tumor diameter ≥ 1 cm, capsular invasion, tumor located at the upper pole and presence of CLNM are independent risk factors for LLNM in PTC. Based on the above risk factors, Nomogram prediction models for CLNM and LLNM are constructed respectively. The AUC of the CLNM prediction model is 91.5%. LLNM model is 96.1%.

Conclusion: Ultrasound indicators, C-TIRADS score combined with BRAF gene status, Tg and clinical indicators of patients have important value in predicting cervical CLNM and LLNM in PTC. The Nomogram prediction models constructed based on the above indicators can effectively predict the risk of LNM in PTC.