OBJECTIVE: This study aimed to develop a high-performance prognostic model to predict poly(ADP-ribose) polymerase inhibitor (PARPi) treatment outcomes in patients with ovarian cancer. METHODS: This was a retrospective cohort study. Inclusion criteria were high-grade serous or endometroid carcinoma, clear cell carcinoma with platinum-sensitive disease (>6 months without progression from the end of platinum) or platinum-responsive disease eligible for front-line PARPi therapy. All collected samples underwent OncoWES-HRD analysis, with an Homologous recombination deficiency (HRD) score threshold set at 39. We performed LASSO regression analysis to develop a predictive model for assessing the effectiveness of PARPi treatment in patients with ovarian cancer. The data were analyzed using R software. RESULTS: We collected primary tumors from 221 Chinese patients with ovarian cancer, of whom 99 patients with high-grade serous ovarian carcinoma received PARPi treatment. Based on the HRD score threshold, 144 patients were classified as HRD-positive and 77 as HRD-negative. We found that the HRD-positive group had higher mutation frequencies of ANKHD1 and MUC16 compared to the HRD-negative group. Furthermore, biomarkers such as clonal mutations, BRCA mutations, high indel burden, and high loss-of-heterozygosity were associated with notably higher HRD scores and longer progression-free survival. Using HRD genomic features, we established a LASSO regression-based risk score model for predicting PARPi treatment outcomes. This model showed promising performance compared to other HRD assessments (the OncoWES-HRD score and the OncoWES-HRD and BRCA metrics), with a higher area under the curve and significantly longer progression-free survival (p< .05) in both training and test cohorts. CONCLUSIONS: We developed a novel prognostic model that can predict PARPi treatment outcomes, offering a valuable tool for identifying patients who may benefit from PARPi therapy in ovarian cancer. However, the model needs further validation.