A novel nomogram based on LODDS to predict the prognosis of epithelial ovarian cancer

// Xue-Lian Xu 1, * , Hao Cheng 3, * , Meng-Si Tang 1, 2 , Hai-Liang Zhang 1 , Rui-Yan Wu 1 , Yan Yu 1 , Xuan Li 1 , Xiu-Min Wang 1 , Jia Mai 1 , Chen-Lu Yang 1, 2 , Lin Jiao 1 , Zhi-Ling Li 1 , Zhen-Mei Zhong 1, 2 , Rong Deng 1 , Jun-Dong Li 1, 2 , Xiao-Feng Zhu 1 1 State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Cancer Center, Sun Yat-sen University, Guangzhou 510060, China 2 Department of Gynecological Oncology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China 3 The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou 510060, China * These authors have contributed equally to this work Correspondence to: Xiao-Feng Zhu, email: zhuxf@sysucc.org.cn Jun-Dong Li, email: lijd@sysucc.org.cn Keywords: LODDS, epithelial ovarian cancer, nomogram, prognosis, SEER Received: July 20, 2016      Accepted: November 22, 2016      Published: December 22, 2016 ABSTRACT BACKGROUND: To develop and validate a nomogram based on log of odds between the number of positive lymph node and the number of negative lymph node (LODDS) in predicting the overall survival (OS) and cancer specific survival (CSS) for epithelial ovarian cancer (EOC) patients. MATERIALS AND METHODS: A total of 10,692 post-operative EOC patients diagnosed between 2004 and 2013 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training (n = 7,021) and validation (n = 3,671) cohorts. Multiple clinical pathological parameters were assessed and compared with outcomes. Parameters significantly correlating with outcomes were used to build a nomogram. Bootstrap validation was subsequently used to assess the predictive value of the model. RESULTS: In the training set, age at diagnosis, race, marital status, tumor location, stage, grade and LODDS were correlated significantly with outcome in both the univariate and multivariate analyses and were used to develop a nomogram. The nomogram demonstrated good accuracy in predicting OS and CSS, with a bootstrap-corrected concordance index of 0.757 (95% CI, 0.746-0.768) for OS and 0.770 (95% CI, 0.759-0.782) for CSS. Notably, in this population our model performed favorably compared to the currently utilized Federation of Gynecology and Obstetrics (FIGO) model, with concordance indices of 0.699 (95% CI, 0.688-0.710, P < 0.05) and 0.719 (95% CI, 0.709- 0.730, P < 0.05) for OS and CSS, respectively. Using our nomogram in the validation cohort, the C-indices were 0.757 (95% CI, 0.741-0.773, P < 0.05, compared to FIGO) for OS and 0.762 (95% CI, 0.746-0.779, P < 0.05, compared to FIGO) for CSS. CONCLUSIONS: LODDS works as an independent prognostic factor for predicting survival in patients with EOC regardless of the tumor stage. By incorporating LODDS, our nomogram may be superior to the currently utilized FIGO staging system in predicting OS and CSS among post-operative EOC patients.