ArticleJournal of inflammation research2025
Combined Pan-Immune-Inflammation Value and Prognostic Nutritional Index as a Prognostic Biomarker for Colorectal Cancer Undergoing Enterectomy.
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 4 papers.
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Who cites it
4 citing papers in PubMed.
- Predictors of Metastatic Lymph Node Burden in Colorectal Cancer: A Negative Binomial Regression Analysis.Life (Basel, Switzerland) · 2026Article
- Systemic Inflammatory Biomarkers as Prognostic Indicators in Metastatic Colorectal Cancer: A Retrospective Study.Medicina (Kaunas, Lithuania) · 2026Observational
- Clinical and pathological indicators for predicting disease-free survival after radical surgery in elderly patients with early-stage triple-negative invasive ductal carcinoma of the breast.Frontiers in oncology · 2026Article
- Pan-immune-inflammation value and prognostic nutritional index can predict the 2-year recurrence of triple-negative breast cancer patients after radical resection.Frontiers in oncology · 2026Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Robust biomarkers are needed to address the prognostic heterogeneity in colorectal cancer (CRC). The pan-immune-inflammation value (PIV) and prognostic nutritional index (PNI) are biomarkers of systemic inflammation and immunonutritional status, respectively. This study aimed to develop and validate a novel combined PIV-PNI score to predict survival in CRC patients undergoing curative resection. Methods: This study included a total of 2116 CRC patients who underwent surgical treatment. The PIV and PNI were evaluated and cut-off values were determined. The PIV-PNI value range was 0 to 2, where 2 represented high PIV (≥ 208.9) and low PNI (≤ 49.05), with high PIV or low PNI indicated by 1 and neither is represented by a 0, respectively. The Cox regression model was used to determine the independent risk factors affecting the prognosis of the patients. A nomogram based on PIV-PNI was constructed, and its performance was evaluated using the C-index, calibration curve, ROC curve, and DCA curve. Finally, the nomogram model was compared with the existing staging models. Results: Patients with higher PIV-PNI scores had a poorer prognosis. In the multivariate analysis, it was found that the PIV-PNI score was an independent predictor for the overall survival rate and disease-free survival rate of CRC patients. The nomogram based on PIV-PNI demonstrated excellent discrimination, calibration, and clinical net benefit. The proposed nomogram performed better than other existing staging systems, as evidenced by its higher AUC value. Conclusion: The PIV-PNI score is a potent, non-invasive prognostic biomarker. The developed nomogram facilitates accurate risk stratification, potentially guiding personalized postoperative surveillance and adjuvant therapy decisions for CRC patients.
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