ArticleJournal of inflammation research2025
Prognostic Enhancement in Gastric Cancer Through the Integration of Inflammatory Indices into the pTNM-Inflammation Staging System (pTNM-I).
Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Immune-inflammatory-fibrinogen score as a novel prognostic biomarker in patients with gastric cancer undergoing radical gastrectomy: a multicenter study.World journal of surgical oncology · 2026Trial
- Hypoxia-inducible factors: Regulatory functions and status as a therapeutic target in gastric cancer (Review).Oncology letters · 2026Review
- Research status and hotspots of the systemic immune-inflammation index: A bibliometric analysis.Medicine · 2026Article
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5 authors.
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Abstract
Background: The prognostic discriminative ability of the pathological tumor-node-metastasis (pTNM) staging system for gastric cancer (GC) still requires further improvement. This study aimed to develop a pTNM-Inflammation (pTNM-I) staging system by integrating pTNM staging with peripheral inflammatory status to enhance the prognostic stratification capability of pTNM. Methods: This study retrospectively analyzed 4,049 patients who underwent curative surgery for GC. Receiver Operating Characteristic (ROC) analysis was used to determine the optimal cutoff values of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) for different pTNM stages, and the pTNM-I staging system was constructed. Kaplan-Meier survival curves were used to evaluate the impact of pTNM-I on prognosis. Cox regression analysis was employed to identify independent risk factors affecting patient outcomes. Finally, a nomogram was constructed based on pTNM-I staging and clinical pathological characteristics. Results: After constructing the pTNM-I staging system based on the optimal cutoff values of NLR, PLR, and SII, the 5-year survival rates for stages I-a to III-c were 97.6%, 88.0%, 84.2%, 92.5%, 77.5%, 71.3%, 74.3%, 45.3%, and 27.5% (P < 0.001). ROC analysis showed that the predictive ability of pTNM-I was superior to that of pTNM (AUC: 0.798 vs 0.743). Cox analysis revealed that pTNM-I was an independent prognostic factor associated with patient outcomes (P < 0.001). The nomogram based on pTNM-I also demonstrated better predictive performance compared to the traditional pTNM staging (AUC: 0.808 vs 0.743). Conclusion: The pTNM-I staging system provided more robust prognostic discriminative ability. As a simple, economical, and routine prognostic tool, it is worthy of clinical application.
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