ArticlePeerJ2026
A retrospective analysis of the prognostic value of nutritional-inflammatory markers for patients with cervical cancer.
Article in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Background: Elevated inflammatory markers are consistently linked to poor outcomes in cancer, whereas favorable nutritional status correlates with improved survival. This retrospective study examined the interaction between nutritional-inflammatory indices and their impact on outcomes in patients with cervical cancer. Methods: Data from 465 cervical cancer patients treated at the Second Affiliated Hospital of Soochow University between January 2015 and June 2025 were retrospectively analyzed. The neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), Prognostic Nutritional Index (PNI), and Naples Prognostic Score (NPS) were calculated. Associations of these indices with overall survival (OS) and progression-free survival (PFS) were assessed using the Kaplan-Meier method and Cox regression models. Prognostic value was further evaluated through construction of a nomogram, with predictive accuracy validated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration analysis. Results: Significant differences between deceased patients and survivors were observed in body mass index (BMI), tumor burden, tumor markers, nutritional/inflammatory indices, pathological characteristics, and treatment status (all Conclusions: PNI was validated as a robust independent prognostic factor for both OS and PFS, while NPS demonstrated independent predictive value specifically for PFS. As easily obtainable nutritional-inflammatory indices, they complement conventional clinicopathological parameters. The integrated prognostic model established in this study shows exploratory potential in estimating OS and PFS. Pending future external validation, it may serve as an adjunctive reference for risk stratification and follow-up optimization.
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