RESUMEN
Background: The advantages of the dissecting the metastatic lymph nodes posterior to the right recurrent laryngeal nerve (LN-prRLN) remain a great deal of controversies in papillary thyroid carcinoma (PTC) patients without clinical evidence. The purpose of our retrospective research was to investigate the predictive factors of the LN-prRLN in cN0 PTC patients. Methods and Materials: Altogether 251 consecutive cN0 PTC participants accepted unilateral or bilateral thyroidectomy accompanied with LN-prRLN dissection between June 2020 and May 2023 were included in the research. Then, univariate and multivariate logical regression analysis were conducted to analyze the relationship between the LN-prRLN and these predictive factors, and a predictive model was also developed. Surgical complications of LN-prRLN dissection were also presented. Results: The rate of LN-prRLN was 17.9% (45/251) in cN0 PTC patients after the analysis of postoperative histology. The age <55 years, multifocality, microcalcification, and BRAFV600E mutation were identified to be predictive factors of LN-prRLN in cN0 PTC patients. The risk score for LN-prRLN was calculated: risk score = 1.192 × (if age <55 years) + 0.808 × (if multifocality) + 1.196 × (if microcalcification in nodule) + 0.918 × (if BRAFV600E mutation in nodule). The rates of the transient hypoparathyroidism and hoarseness were 1.2% (3/251) and 2.0% (5/251), respectively. Conclusion: The age <55 years, multifocality, microcalcification, and BRAFV600E mutation are independent predictors of the LN-prRLN in cN0 PTC patients. An effective predictive model was established for predicting the LN-prRLN in cN0 PTC patients, with the aim to better guide the surgical treatment of PTC. A thorough inspection of the lateral compartment is recommended in PTC patients with risk factors. The multicenter research with long-term follow-up should be carried out to ascertain the optimal surgical approach for patients with PTC.
RESUMEN
BACKGROUND: Papillary thyroid carcinoma (PTC) is the most frequent histological type of thyroid carcinoma. Although an increasing number of diagnostic methods have recently been developed, the diagnosis of a few nodules is still unsatisfactory. Therefore, the present study aimed to develop and validate a comprehensive prediction model to optimize the diagnosis of PTC. METHODS: A total of 152 thyroid nodules that were evaluated by postoperative pathological examination were included in the development and validation cohorts recruited from two centres between August 2019 and February 2022. Patient data, including general information, cytopathology, imprinted gene detection, and ultrasound features, were obtained to establish a prediction model for PTC. Multivariate logistic regression analysis with a bidirectional elimination approach was performed to identify the predictors and develop the model. RESULTS: A comprehensive prediction model with predictors, such as component, microcalcification, imprinted gene detection, and cytopathology, was developed. The area under the curve (AUC), sensitivity, specificity, and accuracy of the developed model were 0.98, 97.0%, 89.5%, and 94.4%, respectively. The prediction model also showed satisfactory performance in both internal and external validations. Moreover, the novel method (imprinted gene detection) was demonstrated to play a role in improving the diagnosis of PTC. CONCLUSION: The present study developed and validated a comprehensive prediction model for PTC, and a visualized nomogram based on the prediction model was provided for clinical application. The prediction model with imprinted gene detection effectively improves the diagnosis of PTCs that are undetermined by the current means.
