Wang Baoxiao, Fan Jianming, Li Yu, Wang Yajing
Department of Otolaryngology, Head and Neck Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University Guangzhou 510000, Guangdong, China.
Department of Otolaryngology, Head and Neck Surgery, The Eighth Affiliated Hospital, Sun Yat-Sen University Shenzhen 518033, Guangdong, China.
Am J Cancer Res. 2025 Jun 15;15(6):2500-2517. doi: 10.62347/CYNY8714. eCollection 2025.
To identify key factors influencing postoperative recurrence in patients with glottic laryngeal squamous cell carcinoma (LSCC) and to develop a predictive model incorporating traditional clinicopathological features and novel inflammatory and immune indicators. This model aims to provide a theoretical foundation for individualized prediction of postoperative recurrence risk and support clinical decision-making.
Clinical and laboratory data were collected from 614 patients with glottic laryngeal cancer who underwent surgery between April 2010 and December 2021. The study included inflammatory and immune-related indicators (such as NLR, PLR, PNI, IL-6, IL-8), alongside traditional clinical features like age, T stage, lymph node metastasis, and degree of differentiation. Univariate and multivariate logistic regression, as well as Cox regression analyses, were performed to identify factors associated with recurrence. A Nomogram model was constructed based on Cox regression results. The model's predictive performance was evaluated using ROC curves, the concordance index (C-index), and calibration curves, with validation conducted in both training and validation cohorts.
Multivariate analysis identified age, T stage, lymph node metastasis, degree of differentiation, IL-6, IL-8, PNI, and PLR as independent factors influencing postoperative recurrence in patients with glottic laryngeal cancer. The Nomogram model demonstrated excellent predictive performance in both the training and validation cohorts, with AUCs for 12-, 24-, and 36-month recurrence-free survival predictions of 0.887, 0.906, and 0.915 (training cohort) and 0.895, 0.906, and 0.907 (validation cohort), respectively. The model's concordance indices were 0.860 and 0.857 in the training and validation groups, respectively. Calibration curves revealed a high degree of agreement between predicted and actual outcomes.
The Nomogram model developed in this study integrates multiple clinical and inflammatory-immune indicators, enabling accurate prediction of 12-, 24-, and 36-month recurrence-free survival rates in post-surgical patients with glottic laryngeal cancer. The model holds significant clinical value, with IL-6, IL-8, and PNI identified as crucial indicators for predicting recurrence risk, providing valuable insights for postoperative follow-up and individualized treatment strategies.
确定影响声门型喉鳞状细胞癌（LSCC）患者术后复发的关键因素，并建立一个整合传统临床病理特征以及新型炎症和免疫指标的预测模型。该模型旨在为术后复发风险的个体化预测提供理论基础，并支持临床决策。
收集2010年4月至2021年12月期间接受手术的614例声门型喉癌患者的临床和实验室数据。该研究纳入了炎症和免疫相关指标（如中性粒细胞与淋巴细胞比值（NLR）、血小板与淋巴细胞比值（PLR）、预后营养指数（PNI）、白细胞介素-6（IL-6）、白细胞介素-8（IL-8）），以及年龄、T分期、淋巴结转移和分化程度等传统临床特征。进行单因素和多因素逻辑回归以及Cox回归分析，以确定与复发相关的因素。基于Cox回归结果构建列线图模型。使用受试者工作特征曲线（ROC曲线）、一致性指数（C指数）和校准曲线评估该模型的预测性能，并在训练队列和验证队列中进行验证。
多因素分析确定年龄、T分期、淋巴结转移、分化程度、IL-6、IL-8、PNI和PLR为影响声门型喉癌患者术后复发的独立因素。列线图模型在训练队列和验证队列中均表现出优异的预测性能，12个月、24个月和36个月无复发生存预测的曲线下面积（AUC）在训练队列中分别为0.887、0.906和0.915，在验证队列中分别为0.895、0.906和0.907。该模型在训练组和验证组中的一致性指数分别为0.860和0.857。校准曲线显示预测结果与实际结果高度一致。
本研究开发的列线图模型整合了多种临床和炎症免疫指标，能够准确预测声门型喉癌术后患者12个月、24个月和36个月的无复发生存率。该模型具有重要的临床价值，其中IL-6、IL-8和PNI被确定为预测复发风险的关键指标，为术后随访和个体化治疗策略提供了有价值的见解。
