Abstract
BACKGROUND: Elevated inflammatory markers are commonly linked to poor prognoses in cancer patients, while optimal nutritional status correlates with improved survival outcomes. This study aimed to explore the interplay between nutritional and inflammatory markers and their impact on postoperative outcomes in ovarian cancer patients through a retrospective analysis. METHODS: Data were retrospectively retrieved from patients diagnosed with ovarian cancer who required surgical intervention at the Department of Obstetrics and Gynecology. Overall survival (OS) and cancer-specific survival (CSS) were monitored during follow-up. Kaplan-Meier survival curves were employed to assess OS and CSS across different patient cohorts, evaluating the prognostic significance of nutritional and inflammatory markers. Nomograms for predicting OS and CSS at one, three, and five years postoperatively were constructed, followed by external validation. RESULTS: The prognostic nutritional index (PNI) and Naples prognostic score (NPS) exhibited a significant correlation with OS and CSS in postoperative ovarian cancer patients (p < 0.05). Analysis indicated that patients with a PNI > 51.2 demonstrated the most favorable survival outcomes. Furthermore, those with a low-NPS (L-NPS) had notably better survival rates compared to their high-NPS (H-NPS) counterparts. Independent OS predictors included age, PNI, NPS, histological type, tumor size, targeted therapy, and diabetes. Similarly, the CSS prediction model incorporated age, NPS, tumor size, targeted therapy, and diabetes. The nomograms demonstrated robust predictive accuracy for three- and five-year survival, though one-year calibration curves showed limited agreement. Despite slightly reduced external validation performance compared to the initial sample, the model maintained strong predictive capability. CONCLUSIONS: The nutritional inflammatory index serves as a key independent prognostic marker for OS and CSS in ovarian cancer patients. Nomograms based on PNI and NPS provide valuable prognostic insights for postoperative management. Incorporating these indices into clinical practice could improve patient stratification and guide personalized treatment plans.