The predictive value of a combined nomogram model integrating ultrasound and fine-needle aspiration cytology for large-number cervical lymph node metastases in clinically node-negative papillary thyroid carcinoma: a propensity score matching analysis

结合超声和细针穿刺细胞学检查的联合列线图模型对临床淋巴结阴性的乳头状甲状腺癌患者颈部淋巴结转移数量较多的预测价值:倾向评分匹配分析

阅读:1

Abstract

BACKGROUND: Large-number cervical lymph node metastases (CLNMs) in clinically node-negative (cN0) papillary thyroid carcinoma (PTC) patients are challenging to detect preoperatively, but significantly influence prognosis and treatment strategies. This study aimed to evaluate the predictive value of preoperative ultrasound (US) and fine-needle aspiration cytology (FNAC) for large-number CLNMs in cN0 PTC patients to guide early detection and treatment planning. METHODS: A retrospective analysis was conducted of cN0 PTC patients who underwent total thyroidectomy and cervical lymphadenectomy across six hospitals from September 2018 to June 2024. Based on the presence or absence of large-number CLNMs, patients were grouped accordingly using 1:1 propensity score matching (PSM) for age and sex. Univariate and multivariate logistic regression analyses were conducted to identify independent predictive US, FNAC, and their combined (US-FNAC) factors. A nomogram was constructed based on the independent predictive factors derived from the US-FNAC combination, and its performance and clinical applicability were subsequently assessed through receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA). RESULTS: The univariate and multivariate logistic regression analyses identified the following independent predictors of large-number CLNMs in PTC patients: escape-like arrangement (P=0.016), nuclear elongation (P<0.001), nuclear outline (P<0.001), nucleolus (P=0.043), size (P<0.001), margin (P=0.003), and calcification (P=0.045). The developed nomogram demonstrated good predictive performance with an area under the curve (AUC) of 0.830 [95% confidence interval (CI): 0.774-0.885] in the training cohort and 0.810 (95% CI: 0.718-0.902) in the test cohort. In the training cohort, the nomogram achieved an accuracy of 76.5%, a sensitivity of 81.4%, and a specificity of 71.8%. While in the test cohort, it achieved an accuracy of 76.7%, a sensitivity of 78.4%, and a specificity of 57.3%. CONCLUSIONS: In cN0 PTC patients, large-number CLNMs are associated with several factors, including size, escape-like arrangement, nuclear elongation, nuclear outline, nucleolus, margin, and calcification. Clinicians should pay closer attention to patients with these risk factors. Further, the nomogram developed using these risk factors exhibited promising predictive performance for large-number CLNMs in PTC patients.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。