Risk Factors for Recurrence in Serous Borderline Ovarian Tumors and Early-Stage Low-Grade Serous Ovarian Carcinoma
Background: Tumor recurrence significantly impacts the quality of life and fertility of patients with serous borderline ovarian tumors (SBOT) and early-stage low-grade serous ovarian carcinoma (LGSOC). This study aims to characterize recurrence patterns, identify independent risk factors for recurre...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Current Oncology |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1718-7729/32/5/263 |
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| Summary: | Background: Tumor recurrence significantly impacts the quality of life and fertility of patients with serous borderline ovarian tumors (SBOT) and early-stage low-grade serous ovarian carcinoma (LGSOC). This study aims to characterize recurrence patterns, identify independent risk factors for recurrence, and develop a nomogram to predict recurrence-free survival (RFS). Methods: We conducted a retrospective case-control study to investigate recurrence in patients undergoing fertility-sparing surgery (FSS) and radical surgery (RS). Logistic regression and Cox regression were used to identify risk factors. Kaplan–Meier analysis was applied to evaluate RFS. A nomogram was developed based on identified variables to predict RFS. Results: Tumor capsule disruption and micropapillary were associated with higher recurrence risk in the FSS group. Non-invasive implants were associated with higher recurrence risk in the RS group. The nomogram prediction model was developed based on identified risk factors. The area under the curve (AUC) for RFS predictions was 0.74 (95% CI: 0.62–0.85) at 3 years and 0.78 (95% CI: 0.67–0.89) at 5 years for the FSS group and 0.87 (95% CI: 0.76–0.98) at 3 years and 0.81 (95% CI: 0.65–0.97) at 5 years for the RS group. Conclusions: We identified the risk factors for recurrence of SBOT and early-stage LGSOC following FSS and RS procedures and developed a predictive model for forecasting RFS. This model provides valuable guidance for patients and clinicians in predicting recurrence risk for patients. |
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| ISSN: | 1198-0052 1718-7729 |