Zhu Jiang, Huang Rui, Hu DaiXing, Dou Yi, Ren HaoYu, Yang ZhiXin, Deng Chang, Xiong Wei, Wang Denghui, Mao Yu, Li Xuesong, Su XinLiang
Department of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.
Department of Anesthesiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.
Onco Targets Ther. 2019 Nov 4;12:9077-9084. doi: 10.2147/OTT.S220926. eCollection 2019.
We aimed to establish a prediction model based on preoperative clinicopathologic features and intraoperative frozen section examination for precise prediction of metastatic involvement of lymph nodes posterior to the right recurrent laryngeal nerve (LN-prRLN) in patients with papillary thyroid carcinoma (PTC).
Clinicopathologic data pertaining to patients with PTC who underwent initial thyroid surgery between July 2015 and December 2017 were collected from electronic medical records. Multivariate logistic regression analysis was performed to identify independent predictors of LN-prRLN metastasis for incorporation in the nomogram. The performance of the model was assessed using discriminative ability, calibration, and clinical application.
A total of 592 patients were enrolled in this study. The LN-prRLN metastatic positivity was 19% (95% confidence interval [CI], 15.61-21.89%). On multivariate analysis, ultrasonography-reported LN status, extrathyroid extension, Delphian lymph node metastasis, and number of metastatic pretracheal and paratracheal lymph nodes were independent predictors of LN-prRLN metastasis. The nomogram showed good discriminative ability (C-index: 0.87; [95% CI, 0.84-0.91]; bias-corrected C-index: 0.86 [through bootstrapping validation]) and was well calibrated. The decision curve analysis indicated potential clinical usefulness of the nomogram.
This study demonstrates that the risk of LN-prRLN metastasis in individual patients can be robustly predicted by a nomogram that integrates readily available preoperative clinicopathologic features and intraoperative frozen section examination. The nomogram may facilitate intraoperative decision-making for patients with PTC.
我们旨在建立一种基于术前临床病理特征和术中冰冻切片检查的预测模型,以精确预测甲状腺乳头状癌(PTC)患者右侧喉返神经后方淋巴结(LN-prRLN)的转移情况。
收集2015年7月至2017年12月期间接受初次甲状腺手术的PTC患者的临床病理数据。进行多因素逻辑回归分析,以确定LN-prRLN转移的独立预测因素,并纳入列线图。使用鉴别能力、校准和临床应用评估该模型的性能。
本研究共纳入592例患者。LN-prRLN转移阳性率为19%(95%置信区间[CI],15.61-21.89%)。多因素分析显示,超声报告的淋巴结状态、甲状腺外侵犯、Delphian淋巴结转移以及气管前和气管旁转移淋巴结数量是LN-prRLN转移的独立预测因素。列线图显示出良好的鉴别能力(C指数:0.87;[95%CI,0.84-0.91];偏差校正C指数:0.86[通过自举验证])且校准良好。决策曲线分析表明列线图具有潜在的临床实用性。
本研究表明,通过整合易于获得的术前临床病理特征和术中冰冻切片检查的列线图,可以可靠地预测个体患者LN-prRLN转移的风险。该列线图可能有助于PTC患者的术中决策。