Li Jiangmiao, Zhao Feng, He Junkun, Zhou Ying, Li Qiyun, Su Jiping
Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Clin Otolaryngol. 2025 Nov;50(6):1040-1052. doi: 10.1111/coa.70012. Epub 2025 Jul 28.
This study aimed to explore the high-risk factors associated with survival outcomes in patients with locally advanced laryngeal cancer (LALC) and to develop and validate a prognostic prediction model. This model aims to identify high-risk patients, assisting in the selection of appropriate treatment options for each individual.
We included 283 patients who were diagnosed with LALC. The LASSO method, XGBoost algorithm, and random forests (RF) were used to screen essential features associated with the prognosis of LALC. A nomogram was then developed based on the COX regression model. Model validation was conducted internally using the bootstrap method. Receiver operating characteristic (ROC), the area under the ROC curve (AUC), the concordance index (C-index), and decision curve analysis (DCA) were used to evaluate model performance. Kaplan-Meier curves compared survival outcomes between different groups and the effectiveness of different treatment methods. All statistical analyses were performed using R statistical software (version 4.3.1).
A total of 484 patients with LALC were followed up. The mean follow-up time was (39.07 ± 30.85) months. The 1-, 3-, and 5-year survival rates of LALC were 79.13%, 62.82%, and 54.34%, respectively. After applying inclusion and exclusion criteria, 283 patients with LALC were finally included. Seven significant variables were identified, and the nomogram incorporating these predictors demonstrated favourable discrimination and calibration. Additionally, the nomogram successfully distinguished patients into low- and high-risk groups. The AUC values for predicting 1-, 3-, and 5-year OS rates were 0.852, 0.850, and 0.829. DCA indicated that the nomogram was clinically useful. The COX model, based on seven features, demonstrated superior performance in predicting 5-year survival outcomes compared to models based on AJCC 8th TNM stage, with NRI as 0.914 and IDI as 0.24.
The Cox regression model developed based on seven independent factors, including 'Age', 'Treatment', 'Surgery', 'DAA', 'K+', 'LNR', and 'TCIS', can effectively predict OS in LALC patients. For LALC patients, especially those in the high-risk group, surgery or surgery combined with adjuvant radiotherapy may offer improved survival benefits.
本研究旨在探讨局部晚期喉癌（LALC）患者生存结局的高危因素，并建立和验证一种预后预测模型。该模型旨在识别高危患者，协助为每个个体选择合适的治疗方案。
我们纳入了283例被诊断为LALC的患者。使用LASSO方法、XGBoost算法和随机森林（RF）筛选与LALC预后相关的关键特征。然后基于COX回归模型开发了一个列线图。使用自助法在内部进行模型验证。采用受试者工作特征（ROC）、ROC曲线下面积（AUC）、一致性指数（C-index）和决策曲线分析（DCA）来评估模型性能。Kaplan-Meier曲线比较了不同组之间的生存结局以及不同治疗方法的有效性。所有统计分析均使用R统计软件（版本4.3.1）进行。
共对484例LALC患者进行了随访。平均随访时间为（39.07±30.85）个月。LALC患者的1年、3年和5年生存率分别为79.13%、62.82%和54.34%。应用纳入和排除标准后，最终纳入283例LALC患者。确定了7个显著变量，包含这些预测因子的列线图显示出良好的区分度和校准度。此外，列线图成功地将患者分为低风险和高风险组。预测1年、3年和5年总生存率的AUC值分别为0.852、0.850和0.829。DCA表明列线图具有临床实用性。基于7个特征的COX模型在预测5年生存结局方面比基于美国癌症联合委员会（AJCC）第8版TNM分期的模型表现更优，净重新分类指数（NRI）为0.914，综合判别改善指数（IDI）为0.24。
基于“年龄”“治疗方式”“手术”“DAA”“血钾”“淋巴结转移率”和“肿瘤累及声门下区”这7个独立因素开发的Cox回归模型，能够有效预测LALC患者的总生存期。对于LALC患者，尤其是高危组患者，手术或手术联合辅助放疗可能带来更好的生存获益。
