甲状腺乳头状癌患者侧颈区淋巴结转移影响因素分析
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1.锦州医科大学公共卫生学院,辽宁 锦州 121000;2.锦州医科大学附属第一医院 普外甲状腺科,辽宁 锦州 121000

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马鑫雨,锦州医科大学公共卫生学院硕士研究生,主要从事临床流行病方面的研究。

基金项目:

生态环境部华南环境研究所环境保护基金资助项目[21111011101EHSM(2020)LNJZ-02]。


Analysis of influencing factors for lateral neck lymph node metastasis in patients with papillary thyroid carcinoma
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1.School of Public Health, Jinzhou Medical University, Jinzhou, Liaoning 121000, China;2.Division of Thyroid Surgery of Department of General Surgery, the First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning 121000, China

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    摘要:

    背景与目的 甲状腺乳头状癌(PTC)是甲状腺癌中占比最大的病理类型,PTC的侧颈区淋巴结转移(LLNM)是导致患者复发和再手术的主要原因。因此,本研究分析PTC患者的临床特征,探讨发生LLNM的影响因素,并构建临床预测模型,为制定合理的手术范围提供参考依据。方法 回顾性分析锦州医科大学附属第一医院2018年3月—2022年1月行手术治疗的PTC患者临床资料,比较发生LLNM与未发生LLNM患者临床病理因素的差异,将有统计学意义的因素纳入多因素Logistic回归分析,用R 4.1.3建立PTC患者LLNM风险预测列线图模型,并绘制校准曲线评价该模型的精准度,用ROC曲线界定LLNM独立危险因素的诊断截断值。结果 共纳入597例PTC患者,其中,187例(31.32%)发生LLNM。单因素分析显示,年龄、肿瘤直径、多发癌灶、腺外侵犯、颈中央区淋巴结转移(CLNM)与BRAFV600E基因突变为PTC患者发生LLNM的影响因素(均P<0.05)。年龄、肿瘤直径、发生腺外侵犯、存在CLNM和BRAFV600E基因突变是LLNM的独立危险因素(均P<0.05)。基于以上影响因素构建PTC患者LLNM风险预测列线图模型。列线图显示,肿瘤直径对LLNM的影响最大,多发癌灶对LLNM的影响最小。校准曲线分析结果显示,该列线图模型预测PTC患者发生LLNM的校准曲线接近于理想曲线。根据ROC曲线显示,LLNM的独立危险因素中肿瘤直径的诊断截断值为1.05 cm,年龄的诊断截断值为32.5岁。结论 存在年龄较小、肿瘤直径>1.05 cm、发生腺外侵犯、存在CLNM和BRAFV600E基因突变等因素的PTC患者发生LLNM的风险相对较高,基于以上因素构建的列线图模型对PTC患者发生侧颈区淋巴结转移具有良好的校准度。当PTC患者存在以上特征且列线图评分较高时,应对其侧颈区淋巴结情况更为谨慎地进行术前评估及术中探查,并采取相应的治疗措施,以改善其预后状况。

    Abstract:

    Background and Aims Papillary thyroid carcinoma (PTC) is the most prevalent pathological type among thyroid cancers, and lateral neck lymph node metastasis (LLNM) in PTC is the primary cause of patient recurrence and reoperation. Therefore, this study was conducted to analyze the clinical characteristics of PTC patients, explore the influencing factors for LLNM, and construct a clinical prediction model to provide a reference basis for determining an appropriate surgical scope.Methods The clinical data of patients with PTC who underwent surgical treatment at the First Affiliated Hospital of Jinzhou Medical University from March 2018 to January 2022 were retrospectively analyzed. The clinicopathologic factors between patients who experienced LLNM and those who did not were compared. Factors that showed statistical significance were included in a multiple Logistic regression analysis. Using R 4.1.3, a predictive nomogram model for LLNM risk in PTC patients was established, and a calibration curve was plotted to evaluate the accuracy of the model. The diagnostic cut-off values for independent risk factors for LLNM were determined using the ROC curve.Results A total of 597 PTC patients were included, and 187 cases (31.32%) had LLNM. Univariate analysis showed that age, tumor diameter, multifocal lesions, extrathyroidal invasion, central neck lymph node metastasis (CLNM), and BRAFV600E gene mutation were significant factors influencing LLNM in PTC patients (all P<0.05). Age, tumor diameter, extrathyroidal invasion, presence of CLNM and BRAFV600E gene mutation were identified as independent risk factors for LLNM (all P<0.05). Based on these factors, a predictive nomogram model for LLNM risk in PTC patients was constructed. The nomogram demonstrated that tumor diameter had the greatest impact on LLNM, while multifocal lesions had the least. Calibration curve analysis indicated that the nomogram model had a close fit to the ideal curve for predicting LLNM in PTC patients. According to the ROC curve analysis, the diagnostic cut-off value for tumor diameter as an independent risk factor for LLNM was 1.05 cm, and the cut-off value for age was 32.5 years.Conclusion PTC patients who have factors such as younger age, tumor diameter >1.05 cm, extrathyroidal extension, presence of CLNM, and BRAFV600E gene mutation have a relatively higher risk of developing LLNM. The nomogram model constructed based on these factors demonstrates good calibration for predicting LLNM in PTC patients. When PTC patients exhibit these characteristics and have high nomogram scores, a more cautious approach should be taken in the preoperative assessment and intraoperative exploration of their lateral neck lymph nodes, and appropriate treatment measures should be implemented to improve their prognosis.

    表 2 LLNM影响因素的二分类Logistic分析Table 2 Binary Logistic analysis of influencing factors for LLNM
    图1 以LLNM二分类Logistic回归模型为基础的列线图Fig.1 Nomogram based on binary Logistic regression model for LLNM
    图2 危险因素诊断LLNM的ROC曲线 A:肿瘤直径;B:年龄Fig.2 ROC curves of risk factors for diagnosing LLNM A: Tumor diameter; B: Age
    图3 LLNM列线图模型的校准曲线Fig.3 Calibration curve for the LLNM nomogram model
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马鑫雨,柴芳,肖智远,刘克毅,郑通,柳晓琳.甲状腺乳头状癌患者侧颈区淋巴结转移影响因素分析[J].中国普通外科杂志,2023,32(5):682-689.
DOI:10.7659/j. issn.1005-6947.2023.05.007

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  • 收稿日期:2022-05-12
  • 最后修改日期:2022-11-16
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  • 在线发布日期: 2023-06-03