A Nomogram Model to Predict Central Lymphnode Metastasis in Thyroid Papillary Carcinoma Suitable for Primary Hospitals
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 1,200
- 试验地点
- 1
- 主要终点
- Multivariate logistic regression analysis
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •underwent TC operation for the first time
- •confirmed as PTC by postoperative pathological examination
- •underwent ipsilateral or bilateral CLND
排除标准
- •complicated with other subtypes of TC or thyroid metastatic cancer
- •received preoperative interventional therapy (such as radiofrequency and microwave therapy) or head and neck radiotherapy
结局指标
主要结局
Multivariate logistic regression analysis
时间窗: 1day
Multivariate logistic regression analysis were conducted to determine the potential nonlinear association between predictors and and the risk of CLNM.
次要结局
未报告次要终点