FCCC LOGO Faculty Publications
Winter WE , Maxwell GL , Tian CQ , Carlson JW , Ozols RF , Rose PG , Markman M , Armstrong DK , Muggia F , McGuire WP
Prognostic factors for stage III epithelial ovarian cancer: A Gynecologic Oncology Group Study
Journal of Clinical Oncology. 2007 Aug;25(24) :3621-3627
PMID: ISI:000249415900013   
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Abstract
Purpose Conflicting results on prognostic factors for advanced epithelial ovarian cancer ( EOC) have been reported because of small sample size and heterogeneity of study population. The purpose of this study was to identify factors predictive of poor prognosis in a similarly treated population of women with advanced EOC. Patients and Methods A retrospective review of demographic, pathologic, treatment, and outcome data from 1,895 patients with International Federation of Gynecology and Obstetrics stage III EOC who had undergone primary surgery followed by six cycles of intravenous platinum/paclitaxel was conducted. A proportional hazards model was used to assess the association of prognostic factors with progression-free survival ( PFS) and overall survival ( OS). Results Increasing age was associated with increased risks for disease progression ( HR = 1.06; 95% CI, 1.02 to 1.11 for an increase every 10 years) and death ( HR = 1.12; 95% CI, 1.06 to 1.18). Mucinous or clear-cell histology was associated with a worse PFS and OS compared with serous carcinomas. Patients with performance status ( PS) 1 or 2 were at an increased risk for recurrence compared with PS 0 ( HR = 1.12; 95% CI, 1.01 to 1.24). Compared with patients with microscopic residual disease, patients with 0.1 to 1.0 cm and > 1.0 cm residual disease had an increased risk of recurrence ( HR = 1.96; 95% CI, 1.70 to 2.26; and HR = 2.36; 95% CI, 2.04 to 2.73, respectively) and death ( HR = 2.11; 95% CI, 1.78 to 2.49; P < .001; and HR = 2.47; 95% CI, 2.09 to 2.92, respectively). Conclusion Age, PS, tumor histology, and residual tumor volume were independent predictors of prognosis in patients with stage III EOC. These data can be used to identify patients with poor prognosis and to design future tailored randomized clinical trials.
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