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| The establishment of a clinico-histopathological predictive model for the risk of cervical lymph node metastasis in papillary thyroid carcinoma |
| LIN Qiongqiong1, ZHAO Zhiguang1, ZHUANG Xiaoping2,HUANG Long3, QIN Haide3, RUAN Honglian4. |
1.Department of Pathology, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; 2.Department of Pathology, Wenzhou Hospital of
Integrated Traditional Chinese and Western Medicine, Wenzhou 325000, China; 3.Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China; 4.School of Public Health, Guangzhou Medical University, Guangzhou
511436, China |
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| Cite this article: |
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LIN Qiongqiong,ZHAO Zhiguang,ZHUANG Xiaoping, et al. The establishment of a clinico-histopathological predictive model for the risk of cervical lymph node metastasis in papillary thyroid carcinoma[J]. JOURNAL OF WEZHOU MEDICAL UNIVERSITY, 2023, 53(4): 276-284.
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Abstract Objective: To explore the risk factors of cervical lymph node metastasis in papillary thyroid carcinoma (PTC), and to construct a predictive model to evaluate the risk of cervical lymph node metastasis (CLNM) in PTC. Methods: A total of 675 patients diagnosed with PTC from April 2020 to July 2022 in our hospital were included. Univariate and multivariate logistic regression analyses were performed to screen out the independent risk factors of cervical lymph node metastasis in PTC. Further more, 70% of the patients were randomly selected for training set (n=474) at a ratio of 7:3, and 30% of patients were selected for validation set (n=201). Then the prediction model was constructed in the training set and validated in the validation dataset. The nomogram was drawn to visualize the model. The receiver operating characteristic (ROC) curves, calibration plot and the decision curve analysis (DCA) were plotted to evaluate the discriminatory ability, calibration and clinical value of the prediction model, respectively. Results: Among 675 patients with PTC, 212 (31.4%) of the patients presented cervical lymph node metastasis. In the multivariate Logistic regression analyses of the risk factors, six variables including age < 45 years, bilaterality, multifocality (≥3 nodules), tumor size > 1 cm,vascular invasion and capsular invasion were turned out to be the risk predictors for CLNM. We developed a visual nomogram based on the predictors that could be used to assess the risk of CLNM in PTC. The model could predict the risk of CLNM, with area under curve (AUC) of 0.787 (95%CI=0.741-0.834) in the training set and AUC of 0.720 (95%CI=0.644-0.796) in the validation set. The DCA showed that the net benefit of nomogram was high when the threshold probability was between 0.2-0.9. Conclusion: The predictive model which is based on risk factors aged < 45 years, bilaterality, multifocality (≥3 nodules), tumor size > 1 cm, vascular invasion and capsular invasion, is confirmed to be helpful to predict the risk of cervical lymph node metastasis in papillary thyroid carcinoma.
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Received: 20 October 2022
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