AUTHOR=Sun Jinxiao , Jiang Qi , Wang Xian , Liu Wenhua , Wang Xin 

TITLE=Nomogram for Preoperative Estimation of Cervical Lymph Node Metastasis Risk in Papillary Thyroid Microcarcinoma

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2021.613974

DOI=10.3389/fendo.2021.613974

ISSN=1664-2392

ABSTRACT=Objective: Accurate preoperative identification of cervical lymph node metastasis (CLNM) is essential for clinical management and established different surgical protocol for patients with papillary thyroid microcarcinoma (PTMC). Herein, we aimed to develop an ultrasound (US) features and clinical characteristics-based nomogram to preoperative diagnosis of CLNM for PTMC.
Method: Our study included 552 patients were pathologically diagnosed as PTMC between January 2015 and June 2019. All patients underwent total thyroidectomy or lobectomy and divided into two groups: CLNM and non-CLNM. Univariate and binary logistic regression were performed to analyze the clinical and ultrasound variables. A nomogram comprising the prognostic model to predict the CLNM was established, and internal validation in the cohort was performed. 
Results: CLNM and non-CLNM were observed in 216(39.1%) and 336(60.9%) cases, respectively. Seven variables of clinical and US features as potential predictors including male (odd ratio [OR] = 1.974, 95% confidence interval [CI], 1.243-2.774; P =0.004), age < 45 years (OR = 4.621, 95% CI, 2.160-9.347; P < 0.001), US-reported CLN status (OR = 1.894, 95% CI, 0.754-3.347; P =0.005), multifocality(OR = 1.793, 95% CI, 0.774-2.649; P =0.007), tumor size ≥ 0.6cm (OR = 1.731, 95% CI,0.793-3.852; P =0.018), ETE (OR = 3.772, 95% CI, 1.752-8.441;P< 0.001) and  microcalcification (OR = 2.316,  95%  CI, 1.099-4.964; P < 0.001). The predictive nomogram was established by involving all the factors above used for preoperative prediction of CLNM in patients with PTCM. The nomogram model showed an AUC of 0.839 and an accuracy of 77.9% in predicting CLNM. Furthermore, the calibration curve demonstrated a strong consistency between nomogram and clinical findings in prediction CLNM for PTMC.
Conclusions: The nomogram achieved promising results for predicting preoperative CLNM in PTMC by combining clinical and US features risk factor. Our proposed prediction model is able to help determine an individual's risk of CLNM in PTMC, thus facilitate reasonable therapy decision making.