Chen-Ye Shao1, Xiao-Long Liu2, Sheng Yao3, Zong-Jie Li3, Zhuang-Zhuang Cong4, Jing Luo5, Guo-Hua Dong6, Jun Yi7. 1. Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China; Department of Thoracic and Cardiovascular Surgery, Nanjing Hospital of Chinese Medicine affiliated to Nanjing University of Chinese Medicine, Nanjing, 210012, China. 2. Department of Cardiothoracic Surgery, Jinling Hospital, Jinling School of Clinical Medicine, Nanjing Medical University, Nanjing, China. 3. Department of Thoracic and Cardiovascular Surgery, Nanjing Hospital of Chinese Medicine affiliated to Nanjing University of Chinese Medicine, Nanjing, 210012, China. 4. Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China. 5. Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China. Electronic address: luojing_2767983@163.com. 6. Department of Thoracic and Cardiovascular Surgery, Nanjing Hospital of Chinese Medicine affiliated to Nanjing University of Chinese Medicine, Nanjing, 210012, China. Electronic address: dongguohua@163.com. 7. Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China. Electronic address: drjunyi@126.com.
Abstract
INTRODUCTION: Survival of patients with the same clinical stage varies widely and effective tools to evaluate the prognosis utilizing clinical staging information is lacking. This study aimed to develop a clinical nomogram for predicting survival of patients with Esophageal Squamous Cell Carcinoma (ESCC). MATERIALS AND METHODS: On the basis of data extracted from the SEER database (training cohort, n = 3375), we identified and integrated significant prognostic factors for nomogram development and internal validation. The model was then subjected to external validation with a separate dataset obtained from Jinling Hospital of Nanjing Medical University (validation cohort, n = 1187). The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index), Akaike information criterion (AIC) and calibration curves. And risk group stratification was performed basing on the nomogram scores. RESULTS: On multivariable analysis of the training cohort, seven independent prognostic factors were identified and included into the nomogram. Calibration curves presented good consistency between the nomogram prediction and actual observation for 1-, 3-, and 5-year OS. The AIC value of the nomogram was lower than that of the 8th edition American Joint Committee on Cancer TNM (AJCC) staging system, whereas the C-index of the nomogram was significantly higher than that of the AJCC staging system. The risk groups stratified by CART allowed significant distinction between survival curves within respective clinical TNM categories. CONCLUSIONS: The risk stratification system presented better discriminative ability for survival prediction than current clinical staging system and might help clinicians in decision making.
INTRODUCTION: Survival of patients with the same clinical stage varies widely and effective tools to evaluate the prognosis utilizing clinical staging information is lacking. This study aimed to develop a clinical nomogram for predicting survival of patients with Esophageal Squamous Cell Carcinoma (ESCC). MATERIALS AND METHODS: On the basis of data extracted from the SEER database (training cohort, n = 3375), we identified and integrated significant prognostic factors for nomogram development and internal validation. The model was then subjected to external validation with a separate dataset obtained from Jinling Hospital of Nanjing Medical University (validation cohort, n = 1187). The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index), Akaike information criterion (AIC) and calibration curves. And risk group stratification was performed basing on the nomogram scores. RESULTS: On multivariable analysis of the training cohort, seven independent prognostic factors were identified and included into the nomogram. Calibration curves presented good consistency between the nomogram prediction and actual observation for 1-, 3-, and 5-year OS. The AIC value of the nomogram was lower than that of the 8th edition American Joint Committee on CancerTNM (AJCC) staging system, whereas the C-index of the nomogram was significantly higher than that of the AJCC staging system. The risk groups stratified by CART allowed significant distinction between survival curves within respective clinical TNM categories. CONCLUSIONS: The risk stratification system presented better discriminative ability for survival prediction than current clinical staging system and might help clinicians in decision making.