Objective To determine the clinical factors affecting Central lymph node metastases(CLNM)of single Papillary thyroid carcinoma(PTC).To predict the value of age for CLNM under different gen-ders and the status of Hashimoto's thyroiditis(HT).Methods The clinical data of 4 115 patients with PTMC(≤10.0 mm)and 664 patients with PTC(＞10.0 mm)in Hangzhou First People's Hospital affiliated to Westlake Uni-versity Medical School from Jan.2010 to Aug.2023 were retrospectively analyzed,and the independent risk factors of PTMC and PTC CLNM were identified by univariate and multivariate logistic regression analysis.According to different gender and HT status,the patients were divided into male group,female group,HT group and non-HT group.The optimal age threshold and diagnostic efficacy of CLNM in each subgroup were determined by Receiver operating characteristic area under the curve(AUC).Results The proportion of CLNM in 3451 PTMCs and 664 PTCs was 27.2％(937/3451)and 58.9％(391/664)(x2=256.565,P＜0.050),respectively.Univariate and multi-variate regression analysis showed that larger tumor(OR 1.230),male(OR 2.085),older age(OR 0.960)and HT(OR 0.697)were independent predictors of the occurrence of CLNM in PTMC.Only male(OR 1.460)and older(OR 0.963)PTC were independently associated with CLNM.Subgroup analysis showed that the age-predicted AUC of CLNM in male,HT and non-HT patients in PTC were higher than that of PTMC,which were 0.642-0.689 and 0.635-0.659,respectively.The age thresholds of female,HT and non-HT subgroups in PTC were lower than those in PTMC,which were 38.5 to 39.5 years old and 41.5 to 42.5 years old,respectively.Conclusions Larg-er tumor,male,older patients and HT can independently predict the risk of CLNM in PTMC,while only male and older people can independently predict the risk of CLNM in PTC.There are certain differences in the age of CLNM occurrence between PTMC and PTC patients with different genders and HT combination status.It is of great signifi-cance to correctly understand these differences for providing personalized clinical treatment.
维普
万方数据