New prognostic index for neoadjuvant chemotherapy outcome in patients with advanced high-grade serous ovarian cancer

Background A validated prognostic index for the outcome of patients with advanced high-grade serous ovarian cancer (HGSOC) undergoing neoadjuvant chemotherapy (NACT) remains elusive. To address this need, we developed an ovarian neoadjuvant chemotherapy prognostic index (ONCPI) to improve predictive accuracy. Methods We encompassed an analysis of the clinicopathological characteristics of patients with advanced HGSOC who were administered platinum-based NACT. Blood inflammatory composite markers were calculated and converted into binary values using optimal cutoffs. Omental hematoxylin and eosin (H&E) stained slides were selected for the assessment of chemotherapy response score (CRS), which served as a measure of NACT efficacy. Logistic regression analysis and Cox proportional hazards regression model were utilized to construct a prognostic index. Results Multivariate logistic analysis showed that both CRS and neutrophil-to-lymphocyte ratio (NLR) independently influenced the response to platinum-based chemotherapy. Meanwhile, Kaplan-Meier and Cox regression analysis revealed that CRS score was significantly correlated with progression-free survival (PFS) and overall survival (OS), and patients with high NLR showed poor OS. We further developed an ovarian neoadjuvant chemotherapy prognostic index (ONCPI) based on the CRS and NLR. The area under the curve (AUC) value of ONCPI was 0.771 (P < 0.001, 95% CI: 0.656-0.887) for the prediction of platinum resistance. This AUC value surpasses that of the individual NLR and CRS, which were 0.670 (P = 0.018, 95% CI: 0.547-0.793) and 0.714 (P = 0.003, 95% CI: 0.590-0.839), respectively. Moreover, survival analysis suggested that patients with ONCPI of 0 and 1 were significantly associated with improved PFS and OS. Conclusions The ONCPI emerges as a significant prognostic marker for predicting NACT outcome in advanced HGSOC patients and holds promise for integration into clinical practice and risk-stratified trial design.