目的 建立基于临床资料、瘤内及瘤周超声特征的列线图模型，预测术前甲状腺微小乳头状癌（papillary thyroid microcarcinoma， PTMC）中央区淋巴结转移（central lymph node metastasis， CLNM）的风险，并评估其预测效能与临床应用价值。 方法 回顾性纳入2022年2月至2023年4月在广州中医药大学第二附属医院接受手术治疗的534例PTMC患者。采用计算机随机数法按7：3比例分为训练集（n = 373）和验证集（n = 161）。根据术后病理结果将患者分为CLNM阳性组与阴性组。收集患者的临床资料及超声影像特征，通过多因素logistic回归分析筛选CLNM的独立危险因素，并据此构建列线图预测模型。采用受试者工作特征曲线（ROC）、曲线下面积（AUC）、校准曲线及决策曲线分析（DCA）评估模型的区分度、校准度及临床实用性。 结果 多因素分析结果显示，男性、年龄< 46.5岁、肿瘤异质性、与甲状腺被膜接触程度≥ 50%以及瘤周回声增强是PTMC发生CLNM的独立危险因素（P < 0.05）。基于上述变量构建的列线图模型在训练集中AUC为0.857（95%CI： 0.820 ~ 0.894），灵敏度为79.0%，特异度为80.0%；在验证集中AUC为0.840（95%CI： 0.778 ~ 0.902），灵敏度为94.1%，特异度为66.7%。校准曲线显示预测概率与实际观察结果具有良好的一致性，DCA表明该模型具有较高的临床净收益。 结论 该研究构建的预测模型整合了性别、年龄及关键超声特征，能够有效预测PTMC患者术前CLNM风险，具有良好的区分度与校准性能，可为个体化手术方案制定提供参考依据。

Objective To develop a nomogram model based on clinical characteristics， intratumoral ultrasound features， and peritumoral ultrasound features for preoperative prediction of the risk of central lymph node metastasis （CLNM） in papillary thyroid microcarcinoma （PTMC）， and to evaluate its predictive performance and clinical applicability. Methods A total of 534 papillary thyroid microcarcinoma （PTMC） cases that underwent surgical treatment at the Second Affiliated Hospital of Guangzhou University of Chinese Medicine from February 2022 to April 2023 were retrospectively included. The cases were randomly allocated in a 7：3 ratio to a training cohort （n = 373） and a validation cohort （n = 161） using computer-generated random numbers. The participants were categorized into central lymph node metastasis （CLNM）-positive and CLNM-negative groups according to postoperative pathological findings. Clinical data and ultrasound imaging features were gathered. Independent risk factors for CLNM were determined using multivariable logistic regression analysis， and a predictive nomogram was developed based on these variables. The performance of the model was evaluated in terms of discrimination， calibration， and clinical utility using receiver operating characteristic （ROC） curves， area under the curve （AUC）， calibration plots， and decision curve analysis （DCA）. Results Multivariate analysis demonstrated that male sex， age less than 46.5 years， tumor heterogeneity， tumor contact with the thyroid capsule equal to or greater than 50%， and peritumoral hyperechoic changes were independent predictors of central lymph node metastasis （CLNM） （P < 0.05）. The nomogram model attained an area under the curve （AUC） of 0.857 （95%CI： 0.820 - 0.894） in the training cohort， with a sensitivity of 79.0% and a specificity of 80.0%. In the validation cohort， the AUC was 0.840 （95%CI： 0.778 - 0.902）， with a sensitivity of 94.1% and a specificity of 66.7%. Calibration plots showed good agreement between predicted and observed probabilities， and decision curve analysis （DCA） indicated a favorable net clinical benefit across a range of threshold probabilities. Conclusions The proposed nomogram， which integrates gender， age， and key ultrasonographic features， can effectively predict the risk of central lymph node metastasis （CLNM） in patients with papillary thyroid microcarcinoma （PTMC） pre-operatively. It demonstrates strong discriminative ability and calibration. This model may serve as a valuable tool for guiding individualized surgical decision-making.