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Published on: October 23, 2020
Prognostic analysis of ovarian cancer patients using the Cox regression model
Quan-Qing Zheng1, Ping Wang, Rong Hui
1Medical School of Xi'an Jiao Tong University, Xi'an, Shannxi, PR China. zhengqq@xjtu.edu.cn
Background And Objective:
Ovarian cancer is one of the most common causes of death among gynecological malignancies. This study was to investigate the prognostic factors for ovarian cancer using the Cox regression model.
Methods:
Data of 103 patients, initially operated at Shaanxi Tumor Hospital from 1999 to 2004, were retrospectively analyzed. All patients were operated by experienced surgeons, and underwent adjuvant chemotherapy afterwards. Follow-up lasted for 100 months. The Kaplan-Meier survival curves, long-rank test and the Cox regression model were used for analysis.
Results:
The one-, three- and five-year survival rates of patients with ovarian cancer were 87.7%, 50.8% and 31.1%, respectively. Univariate analysis and the Cox regression model showed that clinical stage, differentiation, residual tumor size and chemotherapy course were the major prognostic factors of ovarian cancer.
Conclusion:
Sufficient removal of the ovarian cancer at the early stage, residual tumor of less than 2 cm in diameter and sufficient adjuvant chemotherapy after surgery could improve the survival rate of ovarian cancer.
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