ArticleJournal of hepatocellular carcinoma2026
Establishment and Validation of a Nomogram Based on Inflammation-Immunity-Nutrition Biomarker Scores to Predict Postoperative Early Recurrence in Patients with Hepatocellular Carcinoma: A Multicenter Study.
Article in Journal of hepatocellular carcinoma, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Early postoperative recurrence of hepatocellular carcinoma (HCC) significantly impairs patient quality of life and shortens survival. However, existing models rely on single-center or single-dimensional data, making accurate detection of early postoperative HCC recurrence challenging. Thus, designing/evaluating a reliable, non-invasive, comprehensive tool to predict HCC recurrence risk is crucial for guiding postoperative individualized antitumor treatment and improving prognosis. Patients and methods: We retrospectively enrolled patients with HCC (n=1424) receiving curative-intent hepatectomy at the First Affiliated Hospital of Army Medical University of China between December 2012 and December 2022. Patients were randomly stratified into training and testing cohorts in a 7:3 ratio. Using least absolute shrinkage and selection operator (LASSO) logistic and multivariate logistic regression, we screened optimal predictors and subsequently developed a nomogram alongside an online calculator. The prediction model was externally validated at two other medical institutions (n = 218). The area under the curve (AUC) of the receiver operating characteristic, calibration, and decision curves were used to evaluate model performance. Results: The nomogram intuitively showed nine independent risk factors in the prediction model for short-term recurrence in patients with HCC: Edmondson Steiner III-IV, tumor satellite nodules, vascular invasion, largest tumor > 5 cm, alpha-fetoprotein (AFP) level ≥ 400 μg/L, DeRitis ratio ≥ 1.49, gamma-glutamyl transferase (GGT) level ≥ 63.5 U/L, prognostic nutritional index (PNI) < 46.18, and neutrophil-to-lymphocyte ratio (NLR) ≥ 1.91. The AUCs of the training, testing, and validation cohorts were 0.760 (95% CI: 0.731-0.790), 0.784 (95% CI: 0.741-0.828), and 0.787 (95% CI: 0.728-0.846), respectively, indicating good predictive performance. The calibration and decision curves indicated that the model could be translated into tangible clinical benefits. Conclusion: We constructed and evaluated a nomogram based on inflammation-immunity-nutrition biomarker scores to predict early postoperative recurrence of HCC, offering a free, user-friendly online calculator for quick access to results. This calculator empowers clinicians to convert complex clinical data into actionable insights, enabling the design of risk-stratified postoperative management strategies.
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