J Cancer 2018; 9(21):3923-3928. doi:10.7150/jca.26220
A Prognostic Nomogram for Cervical Cancer after Surgery from SEER Database
1. Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, 150086, China;
2. Department of Gynecology Oncology, the Tumor Hospital, Harbin Medical University, Harbin 150086, China.
3. Department of Cardiology, The First Affiliated Hospital of Harbin Medical University, Cardiovascular Institute, Harbin Medical University, Harbin, China
*These authors contributed equally to this work.
Wang C, Yang C, Wang W, Xia B, Li K, Sun F, Hou Y. A Prognostic Nomogram for Cervical Cancer after Surgery from SEER Database. J Cancer 2018; 9(21):3923-3928. doi:10.7150/jca.26220. Available from http://www.jcancer.org/v09p3923.htm
Background: To develop and validate a nomogram based on the conventional measurements and log of odds between the number of positive lymph node and the number of negative lymph node (LODDS) in predicting prognosis for cervical cancer patients after surgery.
Methods: A total of 8202 cervical cancer patients with pathologically confirmed between 2004 and 2014 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. All the patients were divided into training (n=3603) and validation (n=4599) cohorts based on consecutive age of diagnosis. Demographic and clinical pathological factors were evaluated the association with overall survival (OS). Parameters significantly correlating with OS were used to create a nomogram. An independent external validation cohort was subsequently used to assess the predictive performance of the model.
Results: In the training set, age at diagnosis, race, marital status, tumor grade, FIGO stage, histology, size and LODDS were correlated significantly with outcome and used to develop a nomogram. The calibration curve for probability of survival showed excellent agreement between prediction by nomogram and actual observation in the training cohort, with a bootstrap-corrected concordance index of 0.749(95% CI, 0.731-0.767). Importantly, our nomogram performed favorably compared to the currently utilized FIGO model, with concordance indices of 0.786 (95% CI, 0.764 to 0.808) vs 0.685 (95%CI, 0.660 to 0.710) for OS in the validation cohort, respectively.
Conclusions: By incorporating LODDS, our nomogram may be superior to the currently utilized FIGO staging system in predicting OS in cervical cancer patients after surgery.
Keywords: prognostic model, nomogram, cervical cancer, SEER database