Yajie Zhang1, Yuqin Cao1, Jiahao Zhang1, Maosheng Huang2, Pascal Roy3,4,5,6, Binhao Huang7,8,9,10, Hong Yang11, Yi Rong11, Yuping Chen12, Chengchu Zhu13, Wentao Fang7, Zhentao Yu14, Weimin Mao15, Jiaqing Xiang16, Yongtao Han17, Jules Lin18, Jie Zhang7,10, Jianhua Fu11, Jie He19, Hecheng Li1. 1. Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China. 2. Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX. 3. Université de Lyon, Lyon, France. 4. Université Claude Bernard-Lyon 1, Villeurbanne, France. 5. Service de Biostatistique-Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France. 6. Laboratoire de Biométrie et Biologie Évolutive, CNRS UMR 5558, Villeurbanne, France. 7. Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China. 8. Department of Gastric Surgery, Fudan University Shanghai Cancer Center, Shanghai, China. 9. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China. 10. Department of Cardiothoracic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA. 11. Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China. 12. Department of Thoracic Surgery, Cancer Hospital of Shantou University Medical College, Shantou, China. 13. Department of Thoracic Surgery, Taizhou Hospital, Wenzhou Medical University, Taizhou, China. 14. Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and PeKing Union Medical College, Shenzhen, China. 15. Department of Thoracic Surgery, Zhejiang Cancer Hospital, Hangzhou, China. 16. Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China. 17. Department of Thoracic Surgery, Sichuan Cancer Hospital & Research Institute, School of Medicine, University of Electronic Science and Technology of China (UESTC), Chengdu, China. 18. Section of Thoracic Surgery, University of Michigan, Ann Arbor, MI. 19. Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Abstract
OBJECTIVE: This study aimed to propose a revised ypN (r-ypN) classification based on lymph node ratio (LNR) and to examine its prognostic value in postneoadjuvant esophageal cancer. BACKGROUND: A new postneoadjuvant pathologic (ypTNM) staging classification has been introduced for esophageal cancer. However, the ypN classification currently defined by the number of positive lymph nodes is influenced by the extent of lymphadenectomy. METHODS: Data on 7195 esophageal cancer patients receiving neoadjuvant chemoradiation were extracted from the National Cancer Database (NCDB). Four r-ypN stages were defined by 3 LNR thresholds (0%, 10%, and 20% using X-tile software). A revised ypTNM (r-ypTNM) classification was developed by solely changing N categories. Kaplan-Meier method and Cox proportional hazards models were used for survival analyses. Akaike information criterion (AIC) and Harrell's concordance index (C-index) were used to compare the predictive performance of the current and the revised classification. External validation was performed using an independent cohort from the NEOCRTEC5010 clinical trial. RESULTS: Both ypN (P<0.001) and r-ypN (P<0.001) were independent prognostic factors of overall survival (OS) for esophageal cancer patients. Kaplan-Meier curves demonstrated a better discrimination with r-ypN than ypN categories. Within each ypN category (except ypN3), OS was significantly different comparing r-ypN strata; however, there were no differences between ypN strata within each r-ypN category (except r-ypN3). r-ypN (AIC: 60752 vs 60782; C-index: 0.591 vs 0.587) and r-ypTNM (AIC: 60623 vs 60628; C-index: 0.613 vs 0.610) showed better predictive performance than the current staging system, with a lower AIC (better calibration) and higher C-index (improved discrimination). This advantage was also confirmed by external validation using the NEOCRTEC5010 cohort. CONCLUSIONS: LNR showed better performance than ypN in predicting OS of esophageal cancer patients after neoadjuvant chemoradiation and may be an improvement on the current staging system.
OBJECTIVE: This study aimed to propose a revised ypN (r-ypN) classification based on lymph node ratio (LNR) and to examine its prognostic value in postneoadjuvant esophageal cancer. BACKGROUND: A new postneoadjuvant pathologic (ypTNM) staging classification has been introduced for esophageal cancer. However, the ypN classification currently defined by the number of positive lymph nodes is influenced by the extent of lymphadenectomy. METHODS: Data on 7195 esophageal cancer patients receiving neoadjuvant chemoradiation were extracted from the National Cancer Database (NCDB). Four r-ypN stages were defined by 3 LNR thresholds (0%, 10%, and 20% using X-tile software). A revised ypTNM (r-ypTNM) classification was developed by solely changing N categories. Kaplan-Meier method and Cox proportional hazards models were used for survival analyses. Akaike information criterion (AIC) and Harrell's concordance index (C-index) were used to compare the predictive performance of the current and the revised classification. External validation was performed using an independent cohort from the NEOCRTEC5010 clinical trial. RESULTS: Both ypN (P<0.001) and r-ypN (P<0.001) were independent prognostic factors of overall survival (OS) for esophageal cancer patients. Kaplan-Meier curves demonstrated a better discrimination with r-ypN than ypN categories. Within each ypN category (except ypN3), OS was significantly different comparing r-ypN strata; however, there were no differences between ypN strata within each r-ypN category (except r-ypN3). r-ypN (AIC: 60752 vs 60782; C-index: 0.591 vs 0.587) and r-ypTNM (AIC: 60623 vs 60628; C-index: 0.613 vs 0.610) showed better predictive performance than the current staging system, with a lower AIC (better calibration) and higher C-index (improved discrimination). This advantage was also confirmed by external validation using the NEOCRTEC5010 cohort. CONCLUSIONS: LNR showed better performance than ypN in predicting OS of esophageal cancer patients after neoadjuvant chemoradiation and may be an improvement on the current staging system.