Abstract 606P
Background
The poor outcome of serous ovarian cancer is driven mainly by its clinicopathologic characteristics. To address this issue, we developed and validated a nomogram for predicting survival in patients with serous ovarian cancer.
Methods
We extracted data on 4168 incident serous ovarian cancer cases diagnosed in 2010-2015 from the SEER dataset. The 2011-2015 collection of data was used for model development and internal validation. For external validation, the 2010 collection of data sets were used to assess the performance of the model in patients treated. According to the regression model evaluation, we excluded radiation and marital status in the final nomogram without compromising the discriminative ability of the model.
Results
The nomogram illustrated surgery (0 to 100), grade (0 to 75) and chemotherapy (0 to 71.5) sharing the largest contribution to prognosis, followed by the American Joint Committee on Cancer( AJCC.M ,0 to 41), CA125 (0 to 37.0), disease stage (0 to 36) and age (0 to 34) showed a moderate impact on the survival. Race (0 to 21.5), AJCC.N (0 to 13), and laterality (0 to 8) showed a less impact on survival. C-Index for the 12 months were 0.68(95% CI,0.65 to 0.71) and 0.62 (95% CI, 0.60 to 0.64) for the 36 months, 0.63(95% CI,0.60 to 0.66) for the 60 months in the training data set. Kaplan-Meier survival curves and calibration curves (Brier score were 0 to 0.25 in all datasets) showed that nomogram has better discrimination and calibration.
Conclusions
The nomogram provides 1-, 3- and 5-year overall survival (OS) and cancer-specific survival (CSS) predictions for patients with serous ovarian cancer which helps clinicians predict the prognosis of patients and formulate appropriate treatment plans.
Clinical trial identification
Editorial acknowledgement
Dr. Jie Liu, Department of Vascular Surgery, PLA General Hospital.
Legal entity responsible for the study
The author.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.