title: A novel nomogram based on LODDS to predict the prognosis of epithelial ovarian cancer.
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.
  
  
  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.
authors.affiliation: State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Cancer Center, Sun Yat-sen University, Guangzhou 510060, China.
authors.forename: Xue-Lian
authors.initials: XL
authors.lastname: Xu
authors.affiliation: The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou 510060, China.
authors.forename: Hao
authors.initials: H
authors.lastname: Cheng
authors.affiliation: State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Cancer Center, Sun Yat-sen University, Guangzhou 510060, China.|Department of Gynecological Oncology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
authors.forename: Meng-Si
authors.initials: MS
authors.lastname: Tang
authors.affiliation: State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Cancer Center, Sun Yat-sen University, Guangzhou 510060, China.
authors.forename: Hai-Liang
authors.initials: HL
authors.lastname: Zhang
authors.affiliation: State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Cancer Center, Sun Yat-sen University, Guangzhou 510060, China.
authors.forename: Rui-Yan
authors.initials: RY
authors.lastname: Wu
authors.affiliation: State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Cancer Center, Sun Yat-sen University, Guangzhou 510060, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Yu
authors.affiliation: State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Cancer Center, Sun Yat-sen University, Guangzhou 510060, China.
authors.forename: Xuan
authors.initials: X
authors.lastname: Li
authors.affiliation: State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Cancer Center, Sun Yat-sen University, Guangzhou 510060, China.
authors.forename: Xiu-Min
authors.initials: XM
authors.lastname: Wang
authors.affiliation: State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Cancer Center, Sun Yat-sen University, Guangzhou 510060, China.
authors.forename: Jia
authors.initials: J
authors.lastname: Mai
authors.affiliation: State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Cancer Center, Sun Yat-sen University, Guangzhou 510060, China.|Department of Gynecological Oncology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
authors.forename: Chen-Lu
authors.initials: CL
authors.lastname: Yang
authors.affiliation: State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Cancer Center, Sun Yat-sen University, Guangzhou 510060, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Jiao
authors.affiliation: State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Cancer Center, Sun Yat-sen University, Guangzhou 510060, China.
authors.forename: Zhi-Ling
authors.initials: ZL
authors.lastname: Li
authors.affiliation: State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Cancer Center, Sun Yat-sen University, Guangzhou 510060, China.|Department of Gynecological Oncology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
authors.forename: Zhen-Mei
authors.initials: ZM
authors.lastname: Zhong
authors.affiliation: State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Cancer Center, Sun Yat-sen University, Guangzhou 510060, China.
authors.forename: Rong
authors.initials: R
authors.lastname: Deng
authors.affiliation: State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Cancer Center, Sun Yat-sen University, Guangzhou 510060, China.|Department of Gynecological Oncology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
authors.forename: Jun-Dong
authors.initials: JD
authors.lastname: Li
authors.affiliation: State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Cancer Center, Sun Yat-sen University, Guangzhou 510060, China.
authors.forename: Xiao-Feng
authors.initials: XF
authors.lastname: Zhu
mesh_terms: D000368:Aged; D019540:Area Under Curve; D000077216:Carcinoma, Ovarian Epithelial; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D008198:Lymph Nodes / Q000473:pathology* / Q000601:surgery; D008207:Lymphatic Metastasis; D008875:Middle Aged; D015999:Multivariate Analysis; D009367:Neoplasm Staging; D009375:Neoplasms, Glandular and Epithelial / Q000401:mortality / Q000556:secondary* / Q000601:surgery; D049451:Nomograms; D016017:Odds Ratio; D010051:Ovarian Neoplasms / Q000401:mortality / Q000473:pathology* / Q000601:surgery; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D012372:ROC Curve; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D018426:SEER Program; D013997:Time Factors; D016896:Treatment Outcome; D014481:United States
keywords: LODDS; SEER; epithelial ovarian cancer; nomogram; prognosis