Chen Yan, Chen Shaohua, Mei Yujia, Wang Fengwei, Wei Teng, Wei Changyuan
Department of Breast and Thyroid Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People's Republic of China.
Department of Breast and Thyroid Surgery, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, People's Republic of China.
Int J Gen Med. 2025 Aug 6;18:4279-4290. doi: 10.2147/IJGM.S528876. eCollection 2025.
This study aimed to develop and validate a nomogram for predicting occult lateral neck lymph node metastasis (LLNM) in patients with pN1a papillary thyroid carcinoma (PTC), addressing the clinical controversy surrounding prophylactic lateral neck dissection (PLND).
A retrospective analysis was conducted on 128 pN1a PTC patients who underwent total thyroidectomy with bilateral central lymph node dissection and ipsilateral PLND between 2020 and 2023. Clinical and pathological data, including tumor location, size, capsular invasion, and nodal status, were collected. Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression were employed to identify independent risk factors for lymph node metastasis (LNM). A nomogram was constructed based on these factors and internally validated using bootstrap resampling (B=1000). External validation was performed on an additional 37 patients treated between 2023 and 2024.
Tumor location in the upper pole (odds ratio [OR]: 2.45), size >10 mm (OR: 2.12), and capsular invasion (OR: 1.89) were identified as independent predictors of occult LLNM. The nomogram demonstrated robust discriminative ability, with an area under the curve (AUC) of 0.826 (95% confidence interval [CI]: 0.736-0.916) in internal validation and 0.858 (95% CI: 0.740-0.975) in external validation. Calibration curves indicated excellent agreement between predicted and observed outcomes. Decision curve analysis confirmed the model's clinical utility for threshold probabilities exceeding 25%.
The proposed nomogram effectively stratifies the risk of occult LLNM in pN1a PTC patients, providing a valuable tool for individualized surgical planning. By integrating tumor-specific features, this model aids in selecting patients who may benefit from PLND while minimizing overtreatment and associated complications. Further multicenter studies are warranted to enhance its generalizability.
本研究旨在开发并验证一种列线图，用于预测pN1a期甲状腺乳头状癌（PTC）患者隐匿性侧颈淋巴结转移（LLNM），以解决围绕预防性侧颈淋巴结清扫术（PLND）的临床争议。
对2020年至2023年间接受全甲状腺切除术加双侧中央淋巴结清扫术和同侧PLND的128例pN1a期PTC患者进行回顾性分析。收集临床和病理数据，包括肿瘤位置、大小、包膜侵犯和淋巴结状态。采用最小绝对收缩和选择算子（LASSO）回归及多因素逻辑回归来确定淋巴结转移（LNM）的独立危险因素。基于这些因素构建列线图，并使用自抽样法（B = 1000）进行内部验证。对2023年至2024年间接受治疗的另外37例患者进行外部验证。
上极肿瘤位置（比值比[OR]：2.45）、大小>10 mm（OR：2.12）和包膜侵犯（OR：1.89）被确定为隐匿性LLNM的独立预测因素。列线图显示出强大的判别能力，内部验证时曲线下面积（AUC）为0.826（95%置信区间[CI]：0.736 - 0.916），外部验证时为0.858（95%CI：0.740 - 0.975）。校准曲线表明预测结果与观察结果之间具有良好的一致性。决策曲线分析证实该模型对于阈值概率超过25%具有临床实用性。
所提出的列线图有效地对pN1a期PTC患者隐匿性LLNM的风险进行分层，为个体化手术规划提供了有价值的工具。通过整合肿瘤特异性特征，该模型有助于选择可能从PLND中获益的患者，同时将过度治疗及相关并发症降至最低。有必要进行进一步的多中心研究以提高其可推广性。
