开发一种主动监测或手术模型，以预测 cN0 期甲状腺微小乳头状癌的淋巴结转移。

OBJECTIVE: Involvement of multiple lymph node (LN) metastasis in papillary thyroid microcarcinoma (PTMC) may indicate a progressive disease. To assist treatment decision, we conducted a clinical study to develop and validate a prediction model for the preoperative evaluation of LN metastasis involving more than five lymph nodes in patients with clinical N0 (cN0) PTMC. MATERIAL AND METHODS: Using data from 6,337 patients with cN0 PTMCs at Tianjin Medical University Cancer Institute and Hospital from 2013 to 2017, we identified and integrated risk factors for the prediction of multiple LN metastasis to build a nomogram. The predictive accuracy and discriminative ability of the nomogram were evaluated by the concordance index (C-index) and calibration curve. The model was validated using bootstrap resampling of the training cohort and an independent temporal validation cohort at the same institution. RESULTS: In the training cohort (n = 3,209 patients), six independent risk factors were identified and included the prediction model (PTMC Active Surveillance or Surgery (ASOS) Model), including age, gender, multifocality, tumor size, calcification, and aspect ratio. The PTMC ASOS model was validated both internally and through the temporal validation cohort (n = 3,128 patients) from the same institute. The C-indexes of the prediction model in the training cohort were 0.768 (95% CI, 0.698-0.838), 0.768 and 0.771 in the internal validation and external validation cohorts, respectively. The area under the receiver operating characteristic curve (AUC) was 0.7068 and 0.6799. The calibration curve for probability of large-LN metastasis showed good agreement between prediction by nomogram and actual observation. DCA curves were used for comparison with another model, and IDI and NRI were also calculated. The cutoff value of our model was obtained by the ROC curve. Based on this model and cut point, a web-based dynamic nomogram was developed (https://tjmuch-thyroid.shinyapps.io/PTMCASOSM/). CONCLUSION: We established a novel nomogram that can help to distinguish preoperatively cN0 PTMC patients with or without metastasis of multiple lymph nodes. This clinical prediction model may be used in decision making for both active surveillance and surgery.

目的：甲状腺乳头状微小癌（PTMC）中多个淋巴结（LN）转移的参与可能表明疾病进展。为了辅助治疗决策，我们进行了一项临床研究，以开发和验证用于预测临床 N0（cN0）PTMC 患者术前 LN 转移超过五个淋巴结的模型。

材料和方法：使用 2013 年至 2017 年天津医科大学肿瘤医院 6337 例 cN0 PTMC 患者的数据，我们确定并整合了多个 LN 转移预测的风险因素，以构建列线图。通过一致性指数（C-index）和校准曲线评估列线图的预测准确性和判别能力。使用训练队列的 bootstrap 重采样和同一机构的独立时间验证队列对模型进行验证。

结果：在训练队列（n=3209 例）中，确定了 6 个独立的危险因素，并包含预测模型（PTMC 主动监测或手术（ASOS）模型），包括年龄、性别、多灶性、肿瘤大小、钙化和纵横比。PTMC ASOS 模型在内部和同一机构的时间验证队列（n=3128 例）中均得到验证。预测模型在训练队列中的 C 指数分别为 0.768（95%CI，0.698-0.838）、0.768 和 0.771。受试者工作特征曲线（AUC）下面积分别为 0.7068 和 0.6799。大淋巴结转移概率的校准曲线显示了列线图预测与实际观察之间的良好一致性。DCA 曲线用于与另一个模型进行比较，并计算了 IDI 和 NRI。我们的模型的截止值是通过 ROC 曲线获得的。基于该模型和切点，开发了一个基于网络的动态列线图（https://tjmuch-thyroid.shinyapps.io/PTMCASOSM/）。

结论：我们建立了一种新的列线图，可以帮助区分术前 cN0 PTMC 患者是否存在多个淋巴结转移。该临床预测模型可用于主动监测和手术的决策。