The guiding significance of wild-type RET and RET/PTC fusion gene detection in central lymph node dissection of papillary thyroid carcinoma

A total of 300 PTC patients who underwent lymph node dissection in central region from October 2020 to October 2022 were selected, and wild-type RET and RET/PTC fusion gene detection were performed after surgery. They were divided into two groups according to whether central region lymph nodes were metastatic (155 cases) and no metastasis (145 cases). SPSS 25.0 software was used for statistical analysis of the data, and the independent risk factors affecting central lymph node metastasis in PTC patients were analyzed by multi-factor Logistic regression, and a nomogram prediction model was constructed. P<0.05 was considered statistically significant.

There were significant differences in age, lesion diameter, number of lesions, tumor stage, infiltrating membrane, wild type RET, RET/PTC fusion gene, lymph node enlargement and pathological gland and lobe between the two groups (P<0.05). Multivariate Logistic regression analysis suggested that the independent risk factors for central lymph node metastasis in PTC patients were age < 45 years and lesion diameter ≥1.0 cm, number of multiple lesions, stage T_3-4, infiltrating membrane, wild type RET positive, RET/PTC fusion gene positive, lymph node enlargement, unilateral lobe lesions (P<0.05); The results show that the C-index of the training set and the verification set are 0.850 and 0.826, respectively, and the area under the curve of the two sets is 0.802 and 0.789, respectively. When the threshold probability is between 0.01 and 0.91, the net benefit value is higher.

Wild-type RET and RET/PTC fusion genes are independent risk factors for predicting central lymph node metastasis in PTC patients, which can guide clinicians to carefully select the scope of central lymph node dissection for such patients, and construct a nomoimagedata risk prediction model based on clinical characteristics and gene detection characteristics, which has high prediction performance and is helpful to guide clinical decision-making.