Hao Zhang1,2, Yaser Naji1, Minbo Yan1, Wenfei Lian1, Maochun Xie1, Yingbo Dai3,4. 1. Department of Urology, The Fifth Affiliated Hospital, Sun Yat-Sen University, No. 52, Meihua East Road, Zhuhai, 519000, Guangdong, China. 2. Guangdong Provincial Key Laboratory of Biomedical Imaging, The Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, Guangdong, China. 3. Department of Urology, The Fifth Affiliated Hospital, Sun Yat-Sen University, No. 52, Meihua East Road, Zhuhai, 519000, Guangdong, China. daiyb@mail.sysu.edu.cn. 4. Guangdong Provincial Key Laboratory of Biomedical Imaging, The Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, Guangdong, China. daiyb@mail.sysu.edu.cn.
Abstract
BACKGROUND: Predicting the prognosis of patients with adrenocortical carcinoma (ACC) is difficult, due to its unpredictable behavior. The aim of this study is to develop and validate a nomogram to predict survival outcomes in patients with ACC. METHODS: Nomograms were established using the data collected from the Surveillance, Epidemiology, and End Results (SEER) database. Based on univariate and multivariate Cox regression analyses, we identified independent risk factors for overall survival (OS) and cancer-specific survival (CSS). Concordance indexes (c-indexes), the area under the receiver operating characteristics curve (AUC) and calibration curve were used to evaluate predictive performance of these models. The clinical use of nomogram was measured by decision curve analysis (DCA) and clinical impact curves. RESULTS: A total of 855 eligible patients, randomly divided into training (n = 600) and validation cohorts (n = 255), were included in this study. Based on the independent predictors, the nomograms were established and demonstrated good discriminative abilities, with C-indexes for OS and CSS were 0.762 and 0.765 in training cohorts and 0.738 and 0.758 in validation cohorts, respectively. The AUC and calibration plots also demonstrated a good performance for both nomograms. DCA indicated that the two nomograms provide clinical net benefits. CONCLUSION: We unveiled the prognostic factors of ACC and developed novel nomograms that predict OS and CSS more accurately and comprehensively, which can help clinicians improve individual treatment, making proper clinical decisions and adjusting follow-up management strategies.
RCT Entities:
BACKGROUND: Predicting the prognosis of patients with adrenocortical carcinoma (ACC) is difficult, due to its unpredictable behavior. The aim of this study is to develop and validate a nomogram to predict survival outcomes in patients with ACC. METHODS: Nomograms were established using the data collected from the Surveillance, Epidemiology, and End Results (SEER) database. Based on univariate and multivariate Cox regression analyses, we identified independent risk factors for overall survival (OS) and cancer-specific survival (CSS). Concordance indexes (c-indexes), the area under the receiver operating characteristics curve (AUC) and calibration curve were used to evaluate predictive performance of these models. The clinical use of nomogram was measured by decision curve analysis (DCA) and clinical impact curves. RESULTS: A total of 855 eligible patients, randomly divided into training (n = 600) and validation cohorts (n = 255), were included in this study. Based on the independent predictors, the nomograms were established and demonstrated good discriminative abilities, with C-indexes for OS and CSS were 0.762 and 0.765 in training cohorts and 0.738 and 0.758 in validation cohorts, respectively. The AUC and calibration plots also demonstrated a good performance for both nomograms. DCA indicated that the two nomograms provide clinical net benefits. CONCLUSION: We unveiled the prognostic factors of ACC and developed novel nomograms that predict OS and CSS more accurately and comprehensively, which can help clinicians improve individual treatment, making proper clinical decisions and adjusting follow-up management strategies.
Authors: P Icard; P Goudet; C Charpenay; B Andreassian; B Carnaille; Y Chapuis; P Cougard; J F Henry; C Proye Journal: World J Surg Date: 2001-07 Impact factor: 3.352
Authors: Laurent Zini; Umberto Capitanio; Claudio Jeldres; Giovanni Lughezzani; Maxine Sun; Shahrokh F Shariat; Hendrik Isbarn; Philippe Arjane; Hugues Widmer; Paul Perrotte; Markus Graefen; Francesco Montorsi; Pierre I Karakiewicz Journal: BJU Int Date: 2009-06-02 Impact factor: 5.588
Authors: Karel G M Moons; Douglas G Altman; Johannes B Reitsma; John P A Ioannidis; Petra Macaskill; Ewout W Steyerberg; Andrew J Vickers; David F Ransohoff; Gary S Collins Journal: Ann Intern Med Date: 2015-01-06 Impact factor: 25.391