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Model of lymph node metastasis posterior to the right recurrent laryngeal nerve in papillary thyroid carcinoma

机译:甲状腺乳头状癌右喉返神经后淋巴结转移模型

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Background: Cervical lymph node metastasis (LNM) is a prognostic factor of papillary thyroid carcinoma (PTC). The way to deal with lymph node posterior to the right recurrent laryngeal nerve (LN-prRLN) is controversial. Nevertheless, if metastatic lymph nodes are not removed during the first operation, the subsequent salvage surgery of recurrent tumor in this area would entail high risk and complication. The purpose of this study was to develop a preoperative prediction model for LN-prRLN metastasis in PTC patients using clinicopathological characteristics. Patients and methods: We performed a prospective study of 595 patients with PTC who underwent LN-prRLN dissection from March 2014 to June 2017. The clinicopathological data were randomly divided into derivation (n=476) and validation sets (n=119). A predictive model was initially established based upon the data of the derivation set via multivariate analyses, and the accuracy of the model was then examined with data of the validation set. The discriminative power of this model was assessed in both sets. Results: Metastases of the LN-prRLN were identified in 102 (17.14%) of 595 patients. Age (odds ratio [OR] 0.971, 95% CI, 0.949–0.994, p =0.013), tumor size (OR 2.163, 95% CI, 1.431–3.270, p 0.001), capsular invasion (OR 1.934, 95% CI, 1.062–3.522, p =0.031), and right LNM (OR 3.786, 95% CI, 2.012–7.123, p 0.001) were significantly associated with LN-prRLN metastasis. The areas under the curves were 0.790 for the derivation set (sensitivity 71.95%, specificity 78.68%) and 0.878 for the validation set (sensitivity 85.00%, specificity 78.79%). Conclusion: We developed and validated the first model to predict LN-prRLN metastases in patients with PTC based on clinicopathological parameters.
机译:背景:颈淋巴结转移(LNM)是甲状腺乳头状癌(PTC)的预后因素。右喉返神经后方淋巴结的处理方法(LN-prRLN)存在争议。然而,如果在第一次手术中未切除转移性淋巴结,则该区域复发性肿瘤的后续抢救手术将带来高风险和并发症。这项研究的目的是使用临床病理特征为PTC患者开发LN-prRLN转移的术前预测模型。患者和方法:我们对2014年3月至2017年6月行LN-prRLN夹层的595例PTC患者进行了前瞻性研究。临床病理数据随机分为派生(n = 476)和验证集(n = 119)。首先通过多变量分析基于派生集的数据建立预测模型,然后使用验证集的数据检查模型的准确性。两组均评估了该模型的判别力。结果:在595例患者中,有102例(17.14%)发现了LN-prRLN转移。年龄(比值[OR] 0.971,95%CI,0.949–0.994,p = 0.013),肿瘤大小(OR 2.163,95%CI,1.431–3.270,p <0.001),包膜浸润(OR 1.934,95%CI ,1.062–3.522,p = 0.031)和右LNM(OR 3.786,95%CI,2.012–7.123,p <0.001)与LN-prRLN转移显着相关。对于衍生组,曲线下的面积为0.790(敏感性为71.95%,特异性为78.68%),对于验证组,曲线下的面积为0.878(敏感性为85.00%,特异性为78.79%)。结论:我们开发并验证了第一个基于临床病理参数预测PTC患者LN-prRLN转移的模型。

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