Survivorship in advanced ovarian cancer: a prognostic model for overall survival and risk of recurrence
Abstract
Objective
Advanced epithelial ovarian cancer (EOC) poses a significant clinical challenge due to its typically late diagnosis and poor prognosis. However, a subset of patients exhibit remarkably prolonged survival. Identifying prognostic factors and developing tools for estimating outcomes may provide tailored strategies for treatment escalation or de-escalation. This study aimed to identify prognostic factors associated with patient survival and develop a prognostic model estimating EOC patients’ overall survival and risk of recurrence (ROR).
Methods
We conducted a retrospective analysis of 1049 women diagnosed with EOC from January 2002 until June 2024. Clinical, pathological, and molecular data, including germline BRCA pathogenic variants (PVs), and homologous recombination repair analysis were performed. Long-term survivors (LTS), defined as those surviving over 7 or 10 years, and short-term survivors (STS), defined as those surviving less than 2 years were compared. A prognostic model was developed using multivariable logistic regression to estimate survival probabilities and recurrence risk.
Results
Among the study cohort with advanced disease (FIGO stage III-IV), 20.3% survived beyond 7 years and 9.8% beyond 10 years. Factors significantly associated with LTS included younger age, lower disease stage, complete tumor resection, BRCA PV, and treatment with poly (ADP-ribose) polymerase inhibitors. The prognostic model, integrating age, stage, BRCA status, and tumor resection, provided survival estimates and ROR for 2, 5, 7, and 10 years from diagnosis. This tool is based on retrospective logistic regression analysis of long-term and STS across all stages (I-IV).
Conclusions
This study reaffirms established prognostic factors of LTS with advanced EOC and introduces a novel prognostic calculator integrating clinical variables. The tool may assist in personalizing treatment plans and guiding clinical decisions. Validation in multi-institutional cohorts is necessary to confirm its universal utility and applicability.