预测甲状腺乳头状癌右喉返神经后方淋巴结转移的列线图开发与内部验证：一项单中心回顾性研究

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中文摘要

背景： 右喉返神经后方淋巴结（LN-prRLN）位于右侧中央区深部。清扫这些淋巴结在技术上要求高，并增加神经和甲状旁腺损伤的风险，而遗留的隐匿性转移可能导致持续或复发性疾病。提前了解哪些患者可能发生LN-prRLN转移，将有助于调整中央颈淋巴结清扫的范围。
方法： 我们回顾性分析了2021年8月至2026年2月期间在一家三级中心接受治疗的320例甲状腺乳头状癌（PTC）患者，所有患者均接受了甲状腺切除术并右侧中央颈淋巴结清扫，且包括单独采集LN-prRLN。通过单变量logistic回归筛选候选临床病理变量；P<0.05的非冗余二分类变量随后进入多变量Firth惩罚似然模型，使用该模型是因为166例中央淋巴结阴性患者中的单一事件使普通最大似然估计产生偏倚。独立预测因素构成列线图，通过1,000次bootstrap重抽样重复整个模型构建过程进行内部验证，并评估其区分度、校准度（包括乐观校正校准斜率和Hosmer-Lemeshow检验）和临床实用性（决策曲线分析，DCA）。
结果： 320例患者中有51例（15.9%）存在LN-prRLN转移。多灶性（比值比[OR] 4.46）、肿瘤直径>10 mm（OR 2.87）、峡部/近峡部位置（OR 6.28）、中央淋巴结转移（OR 49.15）和侧方淋巴结转移（OR 13.90）是独立预测因素（均P<0.05），而包膜侵犯未被保留（P=0.286）。所得列线图的受试者工作特征曲线下面积为0.936（95%置信区间0.901-0.971；乐观校正后0.927），在最佳截断值0.226时灵敏度为0.843，特异度为0.896。校准曲线接近理想线（乐观校正校准斜率0.961，截距-0.024；Hosmer-Lemeshow P=0.285），DCA显示在临床合理的阈值概率范围内具有正净获益。
结论： 结合多灶性、肿瘤大小、峡部位置以及中央和侧方区转移的列线图以高区分度和可接受的内部校准度预测LN-prRLN转移。该列线图旨在与术前基于影像的淋巴结状态评估一起使用，在常规使用前需要外部验证，最好是多中心前瞻性验证。然而应强调，中央和侧方淋巴结状态——两个最强的预测因素——是根据最终组织病理学报告编码的，并且由于术前超声和增强计算机断层扫描对中央区转移的敏感性有限，此处报告的性能是当列线图使用基于影像的输入时可以预期的上限。

英文摘要

BACKGROUND: The lymph nodes posterior to the right recurrent laryngeal nerve (LN-prRLN) lie deep in the right central compartment. Clearing them is technically demanding and adds to the risk of injury to the nerve and parathyroid glands, whereas occult metastases left behind may cause persistent or recurrent disease. Knowing in advance which patients are likely to have LN-prRLN metastasis would help tailor the extent of central neck dissection.
METHODS: We retrospectively reviewed 320 patients with papillary thyroid carcinoma (PTC) treated at a single tertiary center between August 2021 and February 2026, all of whom underwent thyroidectomy with right central neck dissection that included separate LN-prRLN harvesting. Candidate clinicopathological variables were screened by univariable logistic regression; non-redundant binary variables with P<0.05 then entered a multivariable Firth penalized-likelihood model, used because a single event among the 166 central-node-negative patients biased the ordinary maximum-likelihood estimate. The independent predictors formed a nomogram, internally validated by 1,000 bootstrap resamples that repeated the entire model-building procedure, and was assessed for discrimination, calibration (including the optimism-corrected calibration slope and the Hosmer-Lemeshow test) and clinical utility (decision curve analysis, DCA).
RESULTS: LN-prRLN metastasis was present in 51 of 320 patients (15.9%). Multifocality (odds ratio [OR] 4.46), tumor diameter >10 mm (OR 2.87), isthmic/near-isthmic location (OR 6.28), central lymph node metastasis (OR 49.15) and lateral lymph node metastasis (OR 13.90) were independent predictors (all P<0.05), whereas capsular invasion was not retained (P = 0.286). The resulting nomogram reached an area under the receiver operating characteristic curve of 0.936 (95% confidence interval 0.901-0.971; optimism-corrected 0.927), with a sensitivity of 0.843 and a specificity of 0.896 at the optimal cutoff of 0.226. Calibration curves lay close to the ideal line (optimism-corrected calibration slope 0.961, intercept -0.024; Hosmer-Lemeshow P = 0.285), and DCA showed a positive net benefit across the clinically plausible range of threshold probabilities.
CONCLUSION: A nomogram combining multifocality, tumor size, isthmic location, and central and lateral compartment metastasis predicted LN-prRLN metastasis with high discrimination and acceptable internal calibration. Intended for use with preoperative, imaging-based assessment of nodal status, the nomogram requires external validation, ideally multicenter and prospective, before routine use. It should be emphasized, however, that central and lateral nodal status - the two strongest predictors - were coded from the final histopathological report, and because the sensitivity of preoperative ultrasound and contrast-enhanced computed tomography for central compartment metastasis is limited, the performance reported here is an upper bound on what can be expected when the nomogram is used with imaging-based inputs.