Prediction of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma Based on Multisliecshelieal CT Dual Energy Parameters Combined with Clinical Features
Objective To investigate the value of combining multisliecshelieal CT dual energy parameters with clinical features to predict cervical lymph node metastasis(CLNM)in patients with papillary thyroid carcinoma(PTC).Methods A retrospective analysis was performed for 280 patients with pathologically confirmed papillary thyroid carcinoma.Among them,196 cases were in the training group and 84 cases were in the validation group.Univariate logistic regression was used to analyze the risk factors of CLNM in PTC patients,multivariate logistic regression was used to establish a predictive mod-el,and finally a nomogram was established.The predictive model was evaluated using ROC curves,calibration curves and decision curve analysis.Results Univariate logistic regression analysis showed that calcification,VK and ANCT may be factors affecting CLNM(P＜0.05).Multivariate logistic regression analysis showed that calcification(P=0.03)and AN-CT(P=0.02)were independent predictors of CLNM,and VK(P=0.41)as a risk factor for CLNM and were included in the model to establish a nomogram.The area under the ROC curve(AUC)in the training group was 0.841(95％CI,0.727-0.954).The AUC for the validation group was 0.854(95％CI,0.668-1.000).The nomogram showed good cali-bration and discrimination ability for both the training group and the validation group.Conclusion Calcification,ANCT and VK are three independent predictors of CLNM,which can better predict whether cervical lymph node metastasis occurs in PTC patients,helping clinicians to accurately evaluate preoperative CLNM in PTC patients and providing more basis for a-chieving personalized treatment for PTC patients.
Multisliecshelieal CTPapillary thyroid carcinomaCervical lymph node metastasisNomogram model
NETL
NSTL
万方数据
