The role of contrast enhanced ct plus pan-immune-inflammation value in predicting outcomes of hepatocellular carcinoma after hepatectomy

BACKGROUND: Early recurrence after hepatectomy remains a major challenge for hepatocellular carcinoma (HCC). This study evaluated the prognostic value of preoperative pan-immune-inflammation value (PIV) for predicting early recurrence after curative-intent HCC resection. METHODS: This study retrospectively included 86 patients with histologically confirmed HCC who underwent hepatectomy. The optimal PIV cutoff for predicting recurrence or metastasis within 24 months was determined. Multivariate logistic regression was used to identify predictors of 2-year recurrence. RESULTS: 27 patients experienced recurrence or metastasis within 24 months after surgery. The optimal PIV cutoff was 105.91, with an area under the curve (AUC) of 0.758. Patients with high PIV had shorter progression-free survival than those with low PIV (18.37 months versus 23.93 months). High PIV was independently associated with poorer progression-free survival. High PIV, tumor diameter > 50 mm, and microvascular invasion were independently associated with 2-year recurrence. A three-tier model combining PIV status and tumor size stratified patients into low-, intermediate-, and high-risk groups, with corresponding 24-month recurrence rates of 4.8%, 22.9%, and 60.0%, respectively. The combined predictive model achieved an AUC of 0.861. CONCLUSION: Preoperative PIV may serve as a readily accessible biomarker for identifying patients at increased risk of early recurrence after hepatocellular carcinoma resection.

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Journal
Future Oncology
Published
2026-09-21
DOI
https://doi.org/10.1080/14796694.2026.2735738
Primary Topic
Hepatocellular Carcinoma Treatment and Prognosis
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article
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The role of contrast enhanced ct plus pan-immune-inflammation value in predicting outcomes of hepatocellular carcinoma after hepatectomy

Chunhong Hu, Lu He
Future Oncology
Hepatocellular Carcinoma Treatment and Prognosis
article

The role of contrast enhanced ct plus pan-immune-inflammation value in predicting outcomes of hepatocellular carcinoma after hepatectomy

Chunhong Hu, Lu He
article en

Abstract

BACKGROUND: Early recurrence after hepatectomy remains a major challenge for hepatocellular carcinoma (HCC). This study evaluated the prognostic value of preoperative pan-immune-inflammation value (PIV) for predicting early recurrence after curative-intent HCC resection. METHODS: This study retrospectively included 86 patients with histologically confirmed HCC who underwent hepatectomy. The optimal PIV cutoff for predicting recurrence or metastasis within 24 months was determined. Multivariate logistic regression was used to identify predictors of 2-year recurrence. RESULTS: 27 patients experienced recurrence or metastasis within 24 months after surgery. The optimal PIV cutoff was 105.91, with an area under the curve (AUC) of 0.758. Patients with high PIV had shorter progression-free survival than those with low PIV (18.37 months versus 23.93 months). High PIV was independently associated with poorer progression-free survival. High PIV, tumor diameter > 50 mm, and microvascular invasion were independently associated with 2-year recurrence. A three-tier model combining PIV status and tumor size stratified patients into low-, intermediate-, and high-risk groups, with corresponding 24-month recurrence rates of 4.8%, 22.9%, and 60.0%, respectively. The combined predictive model achieved an AUC of 0.861. CONCLUSION: Preoperative PIV may serve as a readily accessible biomarker for identifying patients at increased risk of early recurrence after hepatocellular carcinoma resection.

Future Oncology
Soochow University (CN), First Affiliated Hospital of Soochow University (CN)
No poverty
Openalex Percentile: Top 13%
Hepatocellular Carcinoma Treatment and Prognosis
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The role of contrast enhanced ct plus pan-immune-inflammation value in predicting outcomes of hepatocellular carcinoma after hepatectomy — Chunhong Hu, Lu He · Future Oncology (2026) | TGRS Research Map | TGRS