目的 探究临床病理特征联合基因检测对甲状腺微小乳头状癌（PTMC）患者隐匿性淋巴结转移（OLNM）的预测价值。 方法 选取2023年5月至2025年5月医院收治的104例PTMC患者，所有患者术前影像学评估均未见可疑淋巴结转移，接受规范性甲状腺切除联合中央区淋巴结清扫术，根据术后病理确诊的中央区淋巴结转移状态分为OLNM阳性组（53例）和OLNM阴性组（51例）。比较两组基线资料、临床病理特征以及BRAF^V600E基因突变、TERT启动子突变情况；通过多因素logistic回归分析PTMC患者发生OLNM的独立危险因素；采用受试者工作特征（ROC）曲线下面积（AUC）评估临床病理特征联合基因检测预测PTMC患者发生OLNM的效能。 结果 与OLNM阴性组患者相比，OLNM阳性组患者术前TSH水平显著升高（P < 0.05）；OLNM阳性组患者肿瘤最大径侵及腺外0.5 cm、多发病灶、存在微钙化、被膜侵犯、侵及腺外、T3分期、BRAF^V600E突变及TERT启动子突变的比例显著更高（均P < 0.05）；术前TSH水平、肿瘤最大径侵及腺外0.5 cm、多发病灶、被膜侵犯、侵及腺外、T分期、BRAF^V600E突变及TERT启动子突变均是PTMC患者发生OLNM的独立危险因素（P < 0.05）；ROC曲线分析显示，临床病理特征（肿瘤最大径、病灶数、微钙化、被膜侵犯、侵及腺外、T分期）联合基因检测（BRAF^V600E、TERT启动子突变）的预测效能最高，AUC为0.940，显著优于单一临床病理特征模型（AUC = 0.736）或单一基因检测模型（BRAF^V600E：AUC = 0.860；TERT：AUC = 0.882）（P < 0.05）。 结论 临床病理特征联合基因检测可显著提升PTMC患者发生OLNM的预测效能，显著优于单一临床病理特征模型或单一基因检测。该联合检测模式可为临床制定个体化手术方案提供重要循证依据，有助于优化PTMC患者的精准治疗决策。

Objective To investigate the predictive value of integrating clinical pathological characteristics with genetic testing for occult lymph node metastasis （OLNM） in patients with papillary thyroid microcarcinoma （PTMC）. Methods A total of 104 PTMC patients admitted to our hospital between May 2023 and May 2025 were included in the study. All patients showed no evidence of suspicious lymph node metastasis on preoperative imaging and underwent standard thyroidectomy with central lymph node dissection. Based on postoperative pathological confirmation of central lymph node metastasis status， patients were classified into an OLNM-positive group （n = 53） and an OLNM-negative group （n = 51）. Baseline characteristics， clinicopathological features， BRAF^V600E gene mutation status， and TERT promoter mutation status were compared between the two groups. To identify factors independently associated with OLNM in PTMC patients， multivariate logistic regression analysis was conducted. The area under the receiver operating characteristic curve （AUC） was utilized to assess the predictive performance of a combined model incorporating clinical， pathological， and genetic features for OLNM. Results Compared with the OLNM-negative group， the OLNM-positive group exhibited significantly higher preoperative thyroid-stimulating hormone （TSH） levels （P < 0.05）. Moreover， the OLNM-positive group demonstrated significantly greater proportions of tumors with diameter > 0.5 cm， multifocality， microcalcifications， capsule invasion， extrathyroidal extension， T3 stage， BRAF^V600E mutation， and TERT promoter mutation （all P < 0.05）. Multivariate logistic regression analysis identified preoperative TSH level， tumor diameter > 0.5 cm， multifocal lesions， capsule invasion， extrathyroidal extension， T stage， BRAF^V600E mutation， and TERT promoter mutation as independent risk factors for OLNM in patients with PTMC （all P < 0.05）. ROC curve analysis demonstrated that the integrated model combining clinical pathological features?including tumor diameter， number of lesions， microcalcification， capsule invasion， extrathyroidal extension， and T stage?with genetic markers （BRAF^V600E and TERT promoter mutations） exhibited the highest predictive performance， yielding an AUC of 0.940. This was significantly higher than the model based solely on clinical pathological features （AUC = 0.736） or those relying exclusively on genetic testing （BRAF^V600E： AUC = 0.860； TERT： AUC = 0.882）， with all comparisons reaching statistical significance （P < 0.05）. Conclusions The integration of clinical pathological features with genetic testing significantly improved the predictive accuracy of OLNM in PTMC patients， surpassing models based solely on individual clinical pathological characteristics or genetic tests alone. This multimodal strategy offers a robust， evidence-based foundation for personalized surgical planning and enhances the precision of clinical decision-making in the management of PTMC.