AUTHOR=Chen Fei , Jiang Shuiping , Yao Fan , Huang Yixi , Cai Jiaxi , Wei Jia , Li Chengxu , Wu Yanxuan , Yi Xiaolin , Zhang Zhen 

TITLE=A nomogram based on clinicopathological and ultrasound characteristics to predict central neck lymph node metastases in papillary thyroid cancer

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 14 - 2023

YEAR=2024

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2023.1267494

DOI=10.3389/fendo.2023.1267494

ISSN=1664-2392

ABSTRACT=Papillary thyroid cancer (PTC) has grown rapidly in prevalence over the past few decades, and central neck lymph node metastasis (CNLNM) is associated with poor prognoses. However, whether to carry out preventive central neck lymph node dissection (CNLND) is still controversial. We aimed to construct a prediction model of NLNM to faciliate making clinical surgical regimens. Methods 691 patients with PTC between Nov 2018 and Dec 2021 were included in our study. Univariate and multivariate analyses were performed on basic information, clinicopathological characteristics, as well as ultrasound characteristics (American College of Radiology, ACR scores). The predictive model was constructed and performed by nomogram, then discriminability, calibrations and clinical applicability were evaluated.Five variables including male, age >55 years, clinical lymph node positivity, tumor size ≥1 cm and ACR scores ≥6 were independent predictors of CNLNM in multivariate analysis, which were eventually included to construct a nomogram model. And the AUC of the model was 0.717, demonstrating great discriminability. A calibration curve was developed to validate the calibration of the present model by bootstrap resampling, which indicated the predicted and actual values were in good agreement and no differentiation from the ideal model. The decision curve analysis (DCA) indicated that the prediction model has good clinical applicability. This is a provisional file, not the final typeset articleOur non-invasive predictive model combines ACR scores with clinicopathological features presented through nomogram and has shown good performance and application prospects for the prediction of CNLNM in PTCs.