A Nomogram Model to Predict Central Lymphnode Metastasis in Thyroid Papillary Carcinoma

Overview

To establish and validate a suitable and practical nomogram for primary hospitals to predict the risk of central lymph node metastasis (CLNM) among thyroid papillary carcinoma (PTC) patients based on clinical and ultrasound characteristics among Chinese population，1000 PTC patients were retrospectively reviewed who underwent bilateral thyroidectomy or lobectomy plus central lymph node dissection（CLND） between June 2014 and September 2019 in Sun Yat-sen Memorial Hospital (Guangzhou, South China), and then LASSO regression analysis was performed to screen out the possible predictors. Another 200 PTC patients from the First Affiliated Hospital of Zhengzhou University (Zhengzhou, North China) who underwent bilateral thyroidectomy or lobectomy plus CLND between March 2019 and November 2020 were enrolled to construct the nomogram. The area under the receiver operating characteristic (ROC) curves (AUC), calibration curves and decision curve analysis (DCA) were used to evaluate the nomogram.

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. 1000 patients were randomized at 7:3 and divided 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. 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 /heliclear / unclear), component (solid /cystic-solid), calcification (absent / microcalcification / macrocalcification), blood flow (absent / internal / annular), cervical lymph node enlargement (absent / present). A nomogram were established for predicting CLNM based on the universally available baseline Characteristics of PTC patients at a tertiary hospital in South China and externally validate it with data from North China. Odd ratios (ORs), 95% confidence 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.