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Updated: Jun 22, 2026

Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence
Published on: November 2, 2014
Prognostic factors predicting recurrence in borderline ovarian tumors
Tzu-I Wu1, Chyi-Long Lee, Meng-Yu Wu
1Department of Obstetrics and Gynecology, Chang Gung Memorial Hospital and Chang Gung University College of Medicine, 5 Fu-Shin St Kueishan, Taoyuan 333, Taiwan.
Objective:
We aimed to investigate outcome of borderline ovarian tumors (BOTs) with respect to methods and extent of surgical approach and to evaluate prognostic factors.
Methods:
A retrospective study included consecutive patients with BOT treated from 1984 to 2008. These cases were confirmed by histological review. The influence of clinico-pathological characteristics upon recurrence and death were analyzed by independent sample t test, Chi-square test, logistic regression model, and Cox proportional hazard model.
Results:
A total of 233 patients were enrolled, 214 in Stage I, 11 Stage II and 8 Stage III. There were 21 relapses, only 5 of which died of disease. 5-year and 10-year overall survival were 97.6% and 96.4%, and recurrence-free survival rates (RFS) were 92.7% and 88.2%, respectively. Median follow-up time for survivors was 81 (range, 0.5-295) months. Median time to recurrence was 31 (range, 5.5-181) months. In multivariate analysis, Stage II/III, cystectomy and higher pretreatment serum CA-125 level (>or=144 U/mL) were selected for a model predicting 5-year RFS, where risk factor=0, 1, and 2-3 had odds ratios of 1, 14.9, and 113.3, respectively (p<0.001). Replacing stage with peritoneal implants, the latter two factors along with invasive peritoneal implants were selected. Of the 5 cases died of disease, all had invasive recurrences. Initial laparoscopic or laparotomy approach had no influence on prognosis.
Conclusions:
Although BOT has an excellent prognosis, they are not exempted from a risk of recurrence. Stage II/III (or invasive implants), cystectomy and higher pre-operative serum CA-125 were independent variables predicting recurrence.

