Jie Cui1, Liping Wang2, Guangmou Tan3, Weiquan Chen4, Guangmin He5, Haiyan Huang6, Zhen Chen7, Hong Yang8, Jie Chen9, Genglong Liu10. 1. Department of Head and Neck Surgery, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095, Guangdong Province, PR China. Electronic address: cuijir8905@163.com. 2. Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, 570102, Hainan Province, PR China. Electronic address: wangliping813@163.com. 3. Department of Head and Neck Surgery, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095, Guangdong Province, PR China. Electronic address: sumstgm@163.com. 4. Department of Head and Neck Surgery, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095, Guangdong Province, PR China. Electronic address: chenweiquan45@sina.com. 5. Department of Ultrasound, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095, Guangdong Province, PR China. Electronic address: 764604608@qq.com. 6. Department of Head and Neck Surgery, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095, Guangdong Province, PR China. Electronic address: huanghaiyan86123@163.com. 7. Department of Intensive Care Unit, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, 528308, Guangdong Province, PR China. Electronic address: jeanyz@foxmail.com. 8. Department of Head and Neck Surgery, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095, Guangdong Province, PR China. Electronic address: hong-yang01@163.com. 9. Department of Head Neck Surgery, Hunan Province Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410000, Hunan Province, PR China. Electronic address: cj1959@126.com. 10. Department of Pathology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095, Guangdong Province, PR China. Electronic address: lglong3@mail2.sysu.edu.cn.
Abstract
BACKGROUND: Recurrence is still major obstacle to long-term survival in laryngeal squamous cell carcinoma (LSCC). We aimed to establish and validate a nomogram to precisely predict recurrence probability in patients with LSCC. METHODS: A total of 283 consecutive patients with LSCC received curative-intend surgery between 2011 and 2014 at were enrolled in this study. Subsequently, 283 LSCC patients were randomly assigned to a training cohort (N = 171) and a validation cohort (N = 112) in a 3:2 ratio. According to the results of multivariable Cox regression analysis in the training cohort, we developed a nomogram. The predictive accuracy and discriminative ability of the nomogram were evaluated by calibration curve and concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate clinical value of our nomogram. RESULTS: Six independent factors rooted in multivariable analysis of the training cohort to predict recurrence were age, tumor site, smoking, alcohol, N stage and hemoglobin, which were all integrated into the nomogram. The calibration curve for the probability of recurrence presented that the nomogram-based predictions were in good correspondence with actual observations. The C-index of the nomogram was 0.81 (0.75-0.88), and the area under curve (AUC) of nomogram in predicting recurrence free survival (RFS) was 0.894, which were significantly better than traditional TNM stage. Decision curve analysis further affirmed that our nomogram had a larger net benefit than TNM stage. The results were confirmed in the validation cohort. CONCLUSION: A risk prediction nomogram for patients with LSCC, incorporating readily assessable clinicopathologic variables, generates more accurate estimations of the recurrence probability when compared TNM stage alone, but still needs additional data before being used in clinical implications.
BACKGROUND: Recurrence is still major obstacle to long-term survival in laryngeal squamous cell carcinoma (LSCC). We aimed to establish and validate a nomogram to precisely predict recurrence probability in patients with LSCC. METHODS: A total of 283 consecutive patients with LSCC received curative-intend surgery between 2011 and 2014 at were enrolled in this study. Subsequently, 283 LSCCpatients were randomly assigned to a training cohort (N = 171) and a validation cohort (N = 112) in a 3:2 ratio. According to the results of multivariable Cox regression analysis in the training cohort, we developed a nomogram. The predictive accuracy and discriminative ability of the nomogram were evaluated by calibration curve and concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate clinical value of our nomogram. RESULTS: Six independent factors rooted in multivariable analysis of the training cohort to predict recurrence were age, tumor site, smoking, alcohol, N stage and hemoglobin, which were all integrated into the nomogram. The calibration curve for the probability of recurrence presented that the nomogram-based predictions were in good correspondence with actual observations. The C-index of the nomogram was 0.81 (0.75-0.88), and the area under curve (AUC) of nomogram in predicting recurrence free survival (RFS) was 0.894, which were significantly better than traditional TNM stage. Decision curve analysis further affirmed that our nomogram had a larger net benefit than TNM stage. The results were confirmed in the validation cohort. CONCLUSION: A risk prediction nomogram for patients with LSCC, incorporating readily assessable clinicopathologic variables, generates more accurate estimations of the recurrence probability when compared TNM stage alone, but still needs additional data before being used in clinical implications.
Authors: Jie Cui; Liping Wang; Waisheng Zhong; Zhen Chen; Jie Chen; Hong Yang; Genglong Liu Journal: Cancer Cell Int Date: 2020-09-29 Impact factor: 5.722