Zuo Huifang, Li Min-Min
Department of Clinical Laboratory Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, People's Republic of China.
J Cancer Res Clin Oncol. 2023 Nov;149(17):15969-15987. doi: 10.1007/s00432-023-05363-0. Epub 2023 Sep 8.
A nomogram is a valuable and easily accessible tool for individualizing cancer prognosis. This study aims to establish and validate two prognostic nomograms for long-term overall survival (OS) and cancer-specific survival (CSS) in non-metastatic nasopharyngeal carcinoma (NPC) patients and to investigate the treatment options for the nomogram-based risk stratification subgroups.
A total of 3959 patients with non-metastatic NPC between 2004 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly allocated to the training and validation cohorts in a 7:3 ratio. Prognostic nomograms were constructed to estimate OS and CSS by integrating significant variables from multivariate Cox regression employing a backward stepwise method. We examined the correlation indices (C-index) and areas under the curves (AUC) of time-dependent receiver operating characteristic curves to assess the discriminative ability of our survival models. The comprehensive enhancements of predictive performance were evaluated with net reclassification operating improvement (NRI) and integrated discrimination improvement (IDI). Reliability was validated using calibration plots. Decision curve analysis (DCA) was used to estimate clinical efficacy and capability. Finally, the nomogram-based risk stratification system used Kaplan-Meier survival analysis and log-rank tests to examine differences between subgroups.
The following independent parameters were significant predictors for OS: sex, age, race, marital status, histological type, median household income, AJCC stage tumor size, and lymph node size. Except for the race variables mentioned above, the rest were independent prognostic factors for CSS. The C-index, AUC, NRI, and IDI indicated satisfactory discriminating properties. The calibration curves exhibited high concordance with the exact outcomes. Moreover, the DCA demonstrated performed well for net benefits. The prognosis significantly differed between low- and high-risk patients (p < 0.001). In a treatment-based stratified survival analysis in risk-stratified subgroups, chemotherapy benefited patients in the high-risk group compared to radiotherapy alone. Radiotherapy only was recommended in the low-risk group.
Our nomograms have satisfactory performance and have been validated. It can assist clinicians in prognosis assessment and individualized treatment of non-metastatic NPC patients.
列线图是一种用于个体化癌症预后评估的有价值且易于获取的工具。本研究旨在建立并验证两个预测非转移性鼻咽癌（NPC）患者长期总生存（OS）和癌症特异性生存（CSS）的预后列线图，并探讨基于列线图风险分层亚组的治疗方案。
从监测、流行病学和最终结果（SEER）数据库中识别出2004年至2015年间共3959例非转移性NPC患者。患者按7:3的比例随机分配至训练队列和验证队列。通过采用向后逐步法整合多变量Cox回归中的显著变量，构建预后列线图以估计OS和CSS。我们检查了时间依赖性受试者工作特征曲线的相关指数（C指数）和曲线下面积（AUC），以评估我们生存模型的判别能力。使用净重新分类操作改善（NRI）和综合判别改善（IDI）评估预测性能的综合提升。使用校准图验证可靠性。决策曲线分析（DCA）用于估计临床疗效和能力。最后，基于列线图的风险分层系统使用Kaplan-Meier生存分析和对数秩检验来检查亚组之间的差异。
以下独立参数是OS的显著预测因素：性别、年龄、种族、婚姻状况、组织学类型、家庭收入中位数、美国癌症联合委员会（AJCC）分期肿瘤大小和淋巴结大小。除上述种族变量外，其余均为CSS的独立预后因素。C指数、AUC、NRI和IDI表明具有令人满意的判别特性。校准曲线与实际结果高度一致。此外，DCA显示净效益表现良好。低风险和高风险患者的预后有显著差异（p < 0.001）。在基于治疗的风险分层亚组生存分析中，与单纯放疗相比，化疗使高风险组患者受益。低风险组建议仅行放疗。
我们的列线图具有令人满意的性能且已得到验证。它可协助临床医生对非转移性NPC患者进行预后评估和个体化治疗。
