Survivorship in Advanced Ovarian Cancer: A Prognostic Model for Overall Survival and Risk of Recurrence
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View abstract on PubMed
Summary
This summary is machine-generated.A new prognostic calculator identifies key factors for long-term survival in advanced epithelial ovarian cancer (EOC), aiding personalized treatment strategies for better patient outcomes.
Area Of Science
- Gynecologic Oncology
- Clinical Cancer Research
- Translational Medicine
Background
- Advanced epithelial ovarian cancer (EOC) presents a significant clinical challenge with late diagnosis and poor prognosis.
- A subset of EOC patients experiences prolonged survival, necessitating identification of prognostic factors.
- Tailored treatment strategies require tools for estimating overall survival (OS) and risk of recurrence (ROR).
Purpose Of The Study
- To identify prognostic factors associated with long-term survival in advanced EOC.
- To develop a prognostic model for estimating OS and ROR in EOC patients.
- To aid in personalized treatment escalation or de-escalation decisions.
Main Methods
- Retrospective analysis of 1,049 women with advanced EOC (FIGO stage III-IV).
- Collected clinical, pathological, and molecular data, including germline BRCA pathogenic variants (PV) and homologous recombination repair analysis.
- Developed a prognostic model using multivariable logistic regression comparing long-term survivors (LTS, >7 or 10 years) and short-term survivors (<2 years).
Main Results
- 20.3% of advanced EOC patients survived beyond 7 years; 9.8% beyond 10 years.
- Factors for LTS included younger age, lower disease stage, complete tumor resection, BRCA PV, and PARP inhibitor treatment.
- The prognostic model integrated age, stage, BRCA status, and tumor resection to estimate survival and ROR at 2, 5, 7, and 10 years.
Conclusions
- Established prognostic factors for LTS in advanced EOC were reaffirmed.
- A novel prognostic calculator integrating clinical variables was developed.
- The tool may assist in personalizing treatment plans and guiding clinical decisions, requiring multi-institutional validation.
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