Objective: In order to predict the survival rate of ovarian cancer patients, multiple independent risk factors are integrated to establish a prognostic nomogram.
Methods: Cox analysis was used to construct the nomogram. All of the mainly independent factors, which can be used to predict 3-year and 5-year survival rates for cancer in the training cohort, were incorporated to establish nomograms. The C-index, operating characteristic, ROC curves, and calibration plots can show evaluation results of performance.
Results: Model derivation was based on 3277 patients who belong to different races. The best threshold for age was 51, 59, and 67 year old and the older the people, the worse their survival. Meanwhile, many lymph node examinations indicated a favorable survival and the survival of the positive set was worse than of that. In addition, the optional threshold was 64 mm for tumor size and the set larger than 64 mm had a better survival than that less than 64 mm. Univariate Cox proportional hazards regression model showed that the similar worse outcomes were showed in black race, advanced grade, stage T3, stage M1, lymph nodes positive, and CA125 positive compared with the first group. We found that the number of lymph nodes examined and tumor size had an inverse relationship with its corresponding score of CSS in training cases with bulking surgery and chemotherapy.
Conclusions: We developed a model which relatively accurately predicted the prognosis of ovarian cancer by multiple univariate analysis, at the same time, the proposed nomograms exhibit superior prognostic discrimination and survival prediction.
Keywords: SEER; nomogram; ovarian cancer; prognosis; surgical therapy; survival.
© 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.