Wu Yinhang, Han Xiaoyang, Li Yan, Zhu Kunli, Yu Jinming
Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Jinan, Shandong 250000, P.R. China.
Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250021, P.R. China.
Oncol Lett. 2020 Feb;19(2):1443-1451. doi: 10.3892/ol.2019.11238. Epub 2019 Dec 23.
The present study aimed to develop two nomograms in order to predict cancer-specific survival (CSS) and overall survival (OS) of patients with anal carcinoma receiving definitive chemoradiotherapy. Data from studies including patients with anal carcinoma, who were determined to be positive histologically and diagnosed between 2004 and 2010, were obtained from the Surveillance, Epidemiology, and End Results database. Significant prognostic factors for CSS and OS of patients were screened to develop nomograms through univariate and multivariate analyses. Nomograms were validated using internal and external data. The predictive abilities of the generated models were evaluated by concordance index (C-index) and calibration curves. Risk stratification was performed for patients with the same TNM stage. A total of 1,473 patients and six independent prognostic factors for CSS and OS, namely age, sex, ethnicity, marital status at diagnosis, T stage and N stage, were included in the nomogram calculations. Calibration curves demonstrated that nomogram prediction was in high accordance with actual observation. The C-indices of nomograms were greater than those of models based on the sixth edition of the American Joint Committee on Cancer TNM staging system for CSS prediction (training cohort, 0.72 vs. 0.70; validation cohort, 0.68 vs. 0.62) and OS (training cohort, 0.70 vs. 0.66; validation cohort, 0.68 vs. 0.62). Survival curves demonstrated significant survival differences among the different risk groups. Nomograms were more accurate than the conventional TNM staging system in prognosis prediction. In addition, survival performances of patients with the same TNM stage could be further distinguished by risk stratification, which provided individualized prediction for patients. These survival prediction methods may aid clinicians in patient counseling and in selecting more individualized therapeutic strategies.
本研究旨在开发两个列线图,以预测接受根治性放化疗的肛管癌患者的癌症特异性生存(CSS)和总生存(OS)。从监测、流行病学和最终结果数据库中获取了2004年至2010年间组织学确诊为肛管癌患者的研究数据。通过单因素和多因素分析筛选出患者CSS和OS的显著预后因素,以开发列线图。使用内部和外部数据对列线图进行验证。通过一致性指数(C指数)和校准曲线评估生成模型的预测能力。对处于相同TNM分期的患者进行风险分层。共有1473例患者以及CSS和OS的六个独立预后因素,即年龄、性别、种族、诊断时的婚姻状况、T分期和N分期,纳入列线图计算。校准曲线表明列线图预测与实际观察高度一致。列线图的C指数在CSS预测方面高于基于美国癌症联合委员会第六版TNM分期系统的模型(训练队列,0.72对0.70;验证队列,0.68对0.62),在OS预测方面也更高(训练队列,0.70对0.66;验证队列,0.68对0.62)。生存曲线显示不同风险组之间存在显著的生存差异。列线图在预后预测方面比传统TNM分期系统更准确。此外,通过风险分层可以进一步区分处于相同TNM分期患者的生存表现,为患者提供个性化预测。这些生存预测方法可能有助于临床医生为患者提供咨询并选择更个性化的治疗策略。