Xiao Luo1, Jianwei Wang2, Min Xu2,3, Xuebin Zou2, Qingguang Lin2, Wei Zheng2, Zhixing Guo2, Anhua Li2, Feng Han2. 1. Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China. 2. Department of Ultrasound and Electrocardiogram, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China. 3. Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China.
Abstract
BACKGROUND: The central lymph node is the most common involvement for papillary thyroid carcinoma (PTC), which is correlated to recurrence and survival. But it is difficult to accurately evaluate lymph node prior to an operation. This retrospective study was designed to develop a risk model and risk stratification to preoperatively predict central lymph node metastasis (CLNM) in PTC and validate this model. METHODS: A series of 1,714 initial treatment PTC patients were enrolled. Among these patients, 1,001 patients were used to develop a predictive model and establish a stratification scoring system. This was validated through the remaining 713 patients. RESULTS: The multivariate analysis revealed that CLNM and lateral lymph node metastasis (LLNM) in ultrasound (US), tumor size, gender, capsule invasion in US, microcalcification and age were significant independent predictors for CLNM. The area under the curve (AUC) of the model was 0.778. Furthermore, the cutoff value to predict CLNM was 8 points, and the sensitivity and specificity were 77% and 65%, respectively. In the scoring system for CLNM, a score of ≤8, 8-18 and >18 were defined as low, intermediate and high risk, respectively. The risk of CLNM was approximately 30%, 60% and 80%, corresponding to the stratification. When validated, the model predicted the risk of CLNM with an AUC of 0.811, a sensitivity and specificity of 83% and 63%, respectively. CONCLUSIONS: This study presented a predictive model to preoperatively assess the risk of CLNM in PTC. The predictive model performed well, but needed to be prospectively validated in external center. 2020 Gland Surgery. All rights reserved.
BACKGROUND: The central lymph node is the most common involvement for papillary thyroid carcinoma (PTC), which is correlated to recurrence and survival. But it is difficult to accurately evaluate lymph node prior to an operation. This retrospective study was designed to develop a risk model and risk stratification to preoperatively predict central lymph node metastasis (CLNM) in PTC and validate this model. METHODS: A series of 1,714 initial treatment PTC patients were enrolled. Among these patients, 1,001 patients were used to develop a predictive model and establish a stratification scoring system. This was validated through the remaining 713 patients. RESULTS: The multivariate analysis revealed that CLNM and lateral lymph node metastasis (LLNM) in ultrasound (US), tumor size, gender, capsule invasion in US, microcalcification and age were significant independent predictors for CLNM. The area under the curve (AUC) of the model was 0.778. Furthermore, the cutoff value to predict CLNM was 8 points, and the sensitivity and specificity were 77% and 65%, respectively. In the scoring system for CLNM, a score of ≤8, 8-18 and >18 were defined as low, intermediate and high risk, respectively. The risk of CLNM was approximately 30%, 60% and 80%, corresponding to the stratification. When validated, the model predicted the risk of CLNM with an AUC of 0.811, a sensitivity and specificity of 83% and 63%, respectively. CONCLUSIONS: This study presented a predictive model to preoperatively assess the risk of CLNM in PTC. The predictive model performed well, but needed to be prospectively validated in external center. 2020 Gland Surgery. All rights reserved.
Entities:
Keywords:
Central lymph node; multivariate analysis; papillary thyroid carcinoma (PTC); prediction; risk model
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