Frontiers in Endocrinology (Jan 2022)

Papillary Thyroid Microcarcinoma: A Nomogram Based on Clinical and Ultrasound Features to Improve the Prediction of Lymph Node Metastases in the Central Compartment

  • Jing Ye,
  • Jia-Wei Feng,
  • Wan-Xiao Wu,
  • Jun Hu,
  • Li-Zhao Hong,
  • An-Cheng Qin,
  • Wei-Hai Shi,
  • Yong Jiang

DOI
https://doi.org/10.3389/fendo.2021.770824
Journal volume & issue
Vol. 12

Abstract

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BackgroundAccurate preoperative identification of central lymph node metastasis (CLNM) is essential for surgical protocol establishment for patients with papillary thyroid microcarcinoma (PTMC). We aimed to develop a clinical and ultrasound characteristics-based nomogram for predicting CLNM.MethodsOur study included 399 patients who were pathologically diagnosed with PTMC between January 2011 and June 2018. Clinical and ultrasound features were collected for univariate and multivariate analyses to determine risk factors of CLNM. A nomogram comprising the prognostic model to predict the CLNM was established, and internal validation in the cohort was performed. The Cox regression model was used to determine the risk factors for recurrence-free survival (RFS) and cumulative hazard was calculated to predict prognosis.ResultsThree variables of clinical and US features as potential predictors including sex (odd ratio [OR] = 1.888, 95% confidence interval [CI], 1.160-3.075; P =0.011), tumor size (OR = 1.933, 95% CI, 1.250-2.990; P =0.003) and ETE (OR = 6.829, 95% CI, 3.250-14.350; P <0.001) were taken into account. The predictive nomogram was established by involving all the factors above used for preoperative prediction of CLNM in patients with PTMC. The nomogram showed excellent calibration in predicting CLNM, with area under curves (AUC) of 0.684 (95% CI, 0.635 to 0.774). Furthermore, tumor size, multifocality, presence of ETE, vascular invasion, and CLNM were the significant factors related to the RFS.ConclusionThrough this easy-to-use nomogram by combining clinical and US risk factor, the possibility of CLNM can be objectively quantified preoperatively. This prediction model may serve as a useful clinical tool to help clinicians determine an individual’s risk of CLNM in PTMC, thus make individualized treatment plans accordingly.

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