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. 2025 May 20:18:6411-6425.
doi: 10.2147/JIR.S521603. eCollection 2025.

Pan-Immune-Inflammation Value as a Prognostic Biomarker for Hepatocellular Carcinoma Patients Undergoing Hepatectomy

Hongyuan Fu  1 , Yubo Wang  2 , Bangde Xiang  1
Affiliations

Pan-Immune-Inflammation Value as a Prognostic Biomarker for Hepatocellular Carcinoma Patients Undergoing Hepatectomy

Hongyuan Fu et al. J Inflamm Res. .

Abstract

Purpose: Hepatocellular carcinoma (HCC) poses a substantial threat to global health, characterized by its high incidence and mortality rates. This research aims to assess the prognostic value of a systematic serum inflammation index, the pan-immune-inflammation value (PIV), in patients with HCC who have undergone hepatectomy.

Patients and methods: A total of 1764 HCC patients who underwent surgery were included in the study. These patients were divided into two groups based on the median PIV value. The Cox regression model was utilized to ascertain the independent risk factors that influence the prognosis of patients. A PIV-based nomogram was constructed and its performance was evaluated by the C-index, calibration curve, ROC curve, and DCA curve. Finally, a comparison was made between the nomogram and existing staging models.

Results: Patients with elevated PIV exhibited diminished OS and RFS compared to those with lower PIV. Univariate and multivariate Cox analyses revealed that PIV is an independent predictor of prognosis. The PIV-based nomogram demonstrated excellent discrimination, calibration, and clinical net benefit. The proposed nomogram outperformed the other existing staging systems, as evidenced by a higher AUC value.

Conclusion: PIV exhibits potential as a prognostic factor for both OS and RFS in patients with HCC who have undergone hepatectomy. The PIV-based nomogram can serve as an additional tool in conjunction with the existing liver cancer staging system, thereby facilitating more personalized treatment decisions for clinicians.

Keywords: hepatectomy; hepatocellular carcinoma; pan-immune- inflammation value; prognosis.

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Conflict of interest statement

The authors state that they have no conflicts of interest to disclose in connection with this research.

Figures

Figure 1
Figure 1
PIV as the best IIB to predict prognosis. (A) ROC curves of different IIBs to predict OS. (B) ROC curves of different IIBs to predict RFS. (C) OS survival curve based on PIV classification. (D) RFS survival curve based on PIV classification.
Figure 2
Figure 2
Construction of prognostic nomograms. (A) The PIV-based nomogram to predict OS. (B) The PIV-based nomogram to predict RFS.
Figure 3
Figure 3
Assessment of the nomogram predicting OS. (A) Calibration curves in the training cohort. (B) Calibration curves in the validation cohort. (C) Time-dependent ROC curves in the training cohort. (D) Time-dependent ROC curves in the validation cohort. (E) DCA curve in the training cohort. (F) DCA curve in the validation cohort.
Figure 4
Figure 4
Assessment of the nomogram predicting RFS. (A) Calibration curves in the training cohort. (B) Calibration curves in the validation cohort. (C) Time-dependent ROC curves in the training cohort. (D) Time-dependent ROC curves in the validation cohort. (E) DCA curve in the training cohort. (F) DCA curve in the validation cohort.
Figure 5
Figure 5
Risk stratification based on the nomograms. (A) K-M survival curves of OS stratification in the training cohort. (B) K-M survival curves of OS stratification in the validation cohort. (C) K-M survival curves of RFS stratification in the training cohort. (D) K-M survival curves of RFS stratification in the validation cohort.
Figure 6
Figure 6
Comparison of predicted outcomes between the nomograms and traditional staging models using the ROC curves. (A) 1-year OS prediction in the training cohort. (B) 1-year OS prediction in the validation cohort. (C) 1-year RFS prediction in the training cohort. (D) 1-year RFS prediction in the validation cohort.
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