目的 探讨超声造影联合端粒酶逆转录酶（TERT）启动子突变构建列线图模型对甲状腺微小乳头状癌（PTMC）并发颈部淋巴结转移（CLNM）的预测能效。 方法 选取2021年1月至2024年3月在我院行甲状腺部分切除或全部切除＋淋巴结清扫的PTMC患者202例，根据术后病理检查是否并发CLNM分为CLNM组（97例）和非CLNM组（105例）。收集所有PTMC患者的一般资料和超声（常规超声和超声造影）特征，采用Sanger测序检测TERT启动子突变。通过单因素和多因素非条件logistic回归分析PTMC并发CLNM的影响因素；RStudio4.4.1软件构建超声造影联合TERT启动子突变的PTMC并发CLNM列线图模型，用校准曲线、决策曲线和C指数评价列线图模型的一致性和净收益，Hosmer-Lemeshow检验列线图模型的拟合优度；MedCalc22.023软件绘制受试者工作特征（ROC）曲线，评价评价超声造影、TERT启动子突变和超声造影联合TERT启动子突变构建列线图模型对PTMC并发CLNM的预测能效。 结果 经术后病理检查，202例PTMC患者CLNM发生率为48.02%（97/202）。单因素分析显示，甲状腺球蛋白抗体、病灶数目、纵横比、微钙化、增强时间、增强方式、增强强度、被膜连续性、TERT启动子突变与PTMC并发CLNM有关（P < 0.05）。多因素非条件Logistic回归显示，多灶性肿瘤（OR = 3.487，95%CI：1.641 ~ 7.406，P = 0.001）、微钙化（OR = 4.484，95%CI：2.113 ~ 9.516，P < 0.001）、等或高增强（OR = 3.187，95%CI：1.460 ~ 6.957，P = 0.004）、被膜连续性中断（OR = 2.201，95%CI：1.051 ~ 4.608，P = 0.036）、TERT启动子突变阳性（OR = 4.460，95%CI：2.132 ~ 10.103，P < 0.001）为PTMC并发CLNM的独立危险因素。根据PTMC并发CLNM的独立危险因素构建超声造影联合TERT启动子突变列线图模型［Logit（P） = -4.486 + 1.350 × 病灶数目 + 1.399 × 微钙化 + 2.124 × 增强强度 + 1.524 × 被膜连续性 + 2.175 × TERT启动子突变］。该列线图模型的C-指数为0.899（95%CI：0.893 ~ 0.905），校准曲线走形接近理想曲线，决策曲线高于两条极端曲线，Hosmer-Lemeshow检验P > 0.05。ROC曲线显示，超声造影联合TERT启动子突变构建的列线图模型预测PTMC并发CLNM曲线下面积为0.899，大于超声造影、TERT启动子突变单独预测的0.857、0.697（P < 0.05）。 结论 超声造影联合TERT启动子突变构建列线图模型对PTMC并发CLNM具有较高的预测能效。

Objective The study aimed to investigate the predictive efficacy of contrast?enhanced ultrasound combined with telomerase reverse transcriptase （TERT） promoter mutation in constructing nomogram model for the prediction of concomitant cervical lymph node metastasis （CLNM） in papillary thyroid microcarcinoma （pTMC）. Methods A total of 202 patients with pTMC who underwent partial or total thyroidectomy + lymph node dissection at our hospital from January 2021 to March 2024 were selected. Then， they were divided into the CLNM group （97 patients） and the non?CLNM group （105 patients） according to whether they had concomitant CLNM on postoperative pathological examination. General data and ultrasound （conventional ultrasound and contrast?enhanced ultrasound） characteristics were collected from all patients with pTMC， and Sanger sequencing was used to detect TERT promoter mutations. The influencing factors of pTMC complicated by CLNM were analyzed by single?factor and multifactorial unconditional logistic regression； the nomogram model of pTMC complicating CLNM with contrast?enhanced ultrasound combined with TERT promoter mutation was constructed by RStudio 4.4.1 software， and the consistency and net benefit of the nomogram model were evaluated by using calibration curves， decision curves， and C?indexes， and the Hosmer?Lemeshow test for goodness of fit of the nomogram model； The predictive efficiency of the nomogram model constructed by combining contrast?enhanced ultrasound and TERT promoter mutation for pTMC complicated by CLNM was evaluated by plotting receiver operating characteristic （ROC） curves using MedCalc22.023 software. Results After postoperative pathological examination， the incidence of CLNM in 202 patients with pTMC was 48.02% （97/202）. Univariate analysis showed that thyroglobulin antibodies， number of lesions， aspect ratio， microcalcifications， enhancement time， enhancement mode， enhancement intensity， capsular continuity， and TERT promoter mutations were associated with pTMC complicating CLNM （P < 0.05）. Multifactorial unconditional logistic regression showed that multifocal tumours （OR = 3.487， 95%CI： 1.641 ~ 7.406， P = 0.001）， microcalcifications （OR = 4.484， 95%CI： 2.113 ~ 9.516， P < 0.001）， equal or high enhancement （OR = 3.187， 95%CI： 1.460 ~ 6.957， P = 0.004）， disruption of peritoneal continuity （OR = 2.201， 95%CI： 1.051 ~ 4.608， P = 0.036）， and TERT promoter mutation positivity （OR = 4.460， 95%CI： 2.132 ~ 10.103， P < 0.001） were the independent risk factors for pTMC complicating CLNM. A contrast?enhanced ultrasound combined TERT promoter mutation nomogram model was constructed based on independent risk factors for pTMC complicating CLNM ［Logit （p）= -4.486 + 1.350 × number of foci + 1.399 × microcalcifications + 2.124 × intensity of enhancement + 1.524 × capsular continuity+2.175 × TERT promoter mutations］. The C?index of this nomogram model was 0.899 （95%CI： 0.893 ~ 0.905）， the calibration curve alignment was close to the ideal curve， the decision curve was higher than the two extreme curves， and the Hosmer?Lemeshow test showed a P > 0.05.The ROC curve analysis showed that the nomogram model constructed with contrast?enhanced ultrasound combined with TERT promoter mutations predicted CLNM in pTMC with an area under the curve of 0.899. This was significantly higher than the area under the curves for contrast?enhanced ultrasound alone （0.857） and TERT promoter mutations alone （0.697） （P < 0.05）. Conclusion The contrast?enhanced ultrasound combined with TERT promoter mutations to construct a nomogram model has high predictive efficiency for pTMC complicating CLNM.