Xu Ziye, Xu Manbin, Sun Zhichen, Feng Qin, Xu Shaowei, Peng Hanwei
Department of Head and Neck Surgery, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Otolaryngology Department of The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Front Oncol. 2025 Mar 17;15:1557459. doi: 10.3389/fonc.2025.1557459. eCollection 2025.
Oral squamous cell carcinoma (OSCC) often presents with unsatisfactory survival outcomes, especially in advanced stages. This study aimed to develop and validate a nomogram incorporating demographic, clinicopathologic, and treatment-related factors to improve the prediction of overall survival (OS) in OSCC patients.
Data from 15,204 OSCC patients in a US database were retrospectively utilized to construct a prognostic model and generate a nomogram. External validation was performed using an independent cohort of 359 patients from a specialized cancer center in China. Prognostic factors were identified using Cox regression analysis and incorporated into the nomogram. Model performance was evaluated by concordance index (C-index), time-dependent area under the receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis (DCA). A risk stratification system was developed to classify patients into high- and low-risk groups.
Age, sex, primary tumor site, T and N staging, and treatment modalities (including surgery, chemotherapy, and radiotherapy) were found to be independent prognostic factors. The nomogram achieved a C-index of 0.727 in the training set and 0.6845 in the validation set, outperforming the conventional TNM staging system. The nomogram's superior predictive accuracy was confirmed by higher AUC values, better calibration, and improved clinical utility as demonstrated by DCA. Risk stratification, based on the nomogram, distinguished patients into distinct prognostic groups with significant OS differences.
This nomogram provides an effective, personalized tool for predicting OS in OSCC. It offers clinicians a valuable aid for treatment decision-making and improves patient management.
口腔鳞状细胞癌（OSCC）的生存结局往往不尽人意，尤其是在晚期。本研究旨在开发并验证一种整合人口统计学、临床病理和治疗相关因素的列线图，以改善对OSCC患者总生存期（OS）的预测。
回顾性利用美国数据库中15204例OSCC患者的数据构建预后模型并生成列线图。使用来自中国一家专业癌症中心的359例患者的独立队列进行外部验证。通过Cox回归分析确定预后因素并纳入列线图。通过一致性指数（C指数）、受试者工作特征曲线下的时间依赖性面积（AUC）、校准图和决策曲线分析（DCA）评估模型性能。开发了一种风险分层系统，将患者分为高风险和低风险组。
年龄、性别、原发肿瘤部位、T和N分期以及治疗方式（包括手术、化疗和放疗）被发现是独立的预后因素。列线图在训练集中的C指数为0.727，在验证集中为0.6845，优于传统的TNM分期系统。列线图更高的AUC值、更好的校准以及DCA显示出的更高临床实用性证实了其优越的预测准确性。基于列线图的风险分层将患者分为不同的预后组，OS存在显著差异。
该列线图为预测OSCC患者的OS提供了一种有效、个性化的工具。它为临床医生的治疗决策提供了有价值的帮助，并改善了患者管理。
