Huaqiang Zhou1, Yaxiong Zhang2, Zeting Qiu3, Gang Chen2, Shaodong Hong2, Xi Chen2, Zhonghan Zhang2, Yan Huang2, Li Zhang4. 1. Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China; State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China. 2. Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China; State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China. 3. Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China; Department of Anesthesia, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. 4. Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China; State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China. Electronic address: zhangli6@mail.sysu.edu.cn.
Abstract
BACKGROUND: The objective of this study was to evaluate the probability of cause-specific death and other causes of death in patients with stage I non-small-cell lung cancer (NSCLC) who underwent surgery. We also built competing risk nomograms to predict the prognosis of patients with NSCLC. PATIENTS AND METHODS: Data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. We identified patients who underwent surgery with stage I NSCLC between 2004 and 2013. We estimated the cumulative incidence function (CIF) for cause-specific death and other causes of death, and tested the differences using Gray's test. The Fine and Gray proportional subdistribution hazard approach was applied to model CIF. We also built competing risk nomograms on the basis of Fine and Gray's model. RESULTS: We identified 20,850 stage I NSCLC patients from 2004 to 2013 in the SEER database. The 5-year cumulative incidence of cause-specific death for stage I NSCLC was 21.9% and 14.2% for other causes of death. Variables associated with cause-specific mortality included age, sex, marital status, histological grade, TNM stage, and surgery. The nomograms were well calibrated, and had good discriminative ability, with a c-index of 0.64 for the cancer-specific mortality model and 0.66 for the competing mortality model. CONCLUSION: We evaluated the CIF of cause-specific death and competing risk death in patients with surgically resected stage I NSCLC using the SEER database. We also built proportional subdistribution models and the first competing risk nomogram to predict prognosis. Our nomograms show a relatively good performance and can be a convenient individualized predictive tool for prognosis.
BACKGROUND: The objective of this study was to evaluate the probability of cause-specific death and other causes of death in patients with stage I non-small-cell lung cancer (NSCLC) who underwent surgery. We also built competing risk nomograms to predict the prognosis of patients with NSCLC. PATIENTS AND METHODS: Data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. We identified patients who underwent surgery with stage I NSCLC between 2004 and 2013. We estimated the cumulative incidence function (CIF) for cause-specific death and other causes of death, and tested the differences using Gray's test. The Fine and Gray proportional subdistribution hazard approach was applied to model CIF. We also built competing risk nomograms on the basis of Fine and Gray's model. RESULTS: We identified 20,850 stage I NSCLCpatients from 2004 to 2013 in the SEER database. The 5-year cumulative incidence of cause-specific death for stage I NSCLC was 21.9% and 14.2% for other causes of death. Variables associated with cause-specific mortality included age, sex, marital status, histological grade, TNM stage, and surgery. The nomograms were well calibrated, and had good discriminative ability, with a c-index of 0.64 for the cancer-specific mortality model and 0.66 for the competing mortality model. CONCLUSION: We evaluated the CIF of cause-specific death and competing risk death in patients with surgically resected stage I NSCLC using the SEER database. We also built proportional subdistribution models and the first competing risk nomogram to predict prognosis. Our nomograms show a relatively good performance and can be a convenient individualized predictive tool for prognosis.