A Nomogram Model to Predict Central Lymphnode Metastasis in Thyroid Papillary Carcinoma Suitable for Primary Hospitals
Conditions: Thyroid Papillary Carcinoma
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

A nomogram model to predict central lymph node metastasis in
thyroid papillary carcinoma suitable for primary hospitals
Date updated：December 6, 2021

Study Protocol and Statistical Analysis Plan
Collection of Patients
1000 Patients who underwent total thyroidectomy or lobectomy and were diagnosed as
PTC by pathological examination between June 2014 and September 2019 in Sun Yat-sen
Memorial Hospital (Guangzhou, South China) and 200 patients in the First Affiliated Hospital
of Zhengzhou University (Zhengzhou, North China) from March 2019 to November 2020 were
selected as the subjects to construct the nomogram. The inclusion criteria were as follow: (1)
underwent TC operation for the first time; (2) confirmed as PTC by postoperative pathological
examination; (3) underwent ipsilateral or bilateral CLND. The exclusion criteria included: (1)
complicated with other subtypes of TC or thyroid metastatic cancer; (2) received preoperative
interventional therapy (such as radiofrequency and microwave therapy) or head and neck
radiotherapy. the patient recruitment process was showed in Figure1. We randomized them at
7:3 and divided it into a training set and a verification set. Besides, 200 cases that met the
inclusion and exclusion criteria
above-mentioned in the First affiliated Hospital of Zhengzhou University were enrolled as
a external verification set.
Fig 1. the recruitment process of patients in our study

Surgical Quality Control
All surgeries were conducted by at least two experienced surgeons. According to the
preoperative US, all patients in our study underwent total thyroidectomy or lobectomy plus
ipsilateral or bilateral CLND and then all specimens were for paraffifin fifixation and
histological analysis in the department of pathology.
Baseline Characteristics
The following clinical features for each patient were obtained before surgery: gender, age,
occupation, complicated with autoimmune diseases (absent / present), history of radiation
exposure (absent / present), family history of thyroid cancer (absent / present), with other
tumors (absent / present) and preoperative laboratory examinations including neutrophil count,
lymphocyte count, platelet count, thyroid-stimulating hormone (TSH), free triiodothyronine
(fT3), free thyroxine (fT4), anti-thyroglobulin antibody (TgAb), thyroid peroxidase antibody
(TPOAb).
Preoperative US signatures of thyroid tumors were also included: distribution (unilateral /
bilateral), shape (regular / irregular), maximum diameter, number (single / multiple), boundary
（clear / semiclear / unclear), component (solid /cystic-solid), calcification (absent /
microcalcification / macrocalcification), blood flow (absent / internal / annular), cervical lymph
node enlargement (absent / present).
Statistical Analysis
Statistical analyses were performed using the Statistical Package for the Social Sciences
(SPSS) for Windows, Version 25.0 and RStudio software. All tests were two-tailed, and a
two-sided value of P<0.05 was considered statistically significant.The continuous variables for
normally distributed data are presented as the mean ± standard deviation (SD) and were
compared by using the Student’s t-test. For the variables with a skewed distribution, data are
presented as medians (interquartile ranges).The categorical variables are reported as frequencies
(percentages), and the characteristics of the patients were compared by using the chi-square test.
A least absolute shrinkage and selection operator (LASSO) regression analysis conducted by
using the glmnet R package was applied to identify the important preoperative predictors
associated with CLNM in the training set, with the optimal values of the penalty parameter λ
determined via 10-fold cross-validation. Multivariate logistic regression analysis were
conducted to determine the potential nonlinear association between predictors and and the risk
of CLNM. Odd ratios (ORs), 95% confifidence interval (CI) and probability values were
obtained by logistic regression analysis. The area under the receiver operating characteristic
(ROC) curve (AUC) was calculated to evaluate the accuracy of the nomogram for predicting
CLNM. The calibration curve and Hosmer-Lemeshow tests were performed to evaluate the
calibration of the nomogram. The decision curve analysis (DCA) was applied to validate clinical
utility of the nomogram.