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Prognostic Significance of PET− SUVmax in Patients Undergoing Resection of Hepatocellular Carcinoma >3 cm: A Retrospective Analysis

Oct 2026 · Anticancer Research · Vol 46, pp. 5577 - 5588 · 0 citations · 15 references
Medicine

TL;DR

Preoperative FDG-PET SUVmax is an independent prognostic biomarker in HCC >3 cm and, when combined with serum alpha-fetoprotein, may enhance prediction of MVI and support incorporating metabolic imaging into preoperative risk models to guide management.

Abstract

Background/Aim: Hepatocellular carcinoma (HCC) >3 cm often recurs after resection, indicating a need for better risk stratification. The 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) maximum standardized uptake value (SUVmax) reflects tumor aggressiveness. We evaluated the prognostic significance of preoperative FDG-PET SUVmax in patients with resectable initial HCC >3 cm. Patients and Methods: This retrospective analysis included 136 patients who underwent curative resection for HCC >3 cm between 2012 and 2021 with preoperative FDG-PET imaging. Patients were classified into low- and high-uptake groups based on tumor SUVmax. Associations between FDG-PET findings and clinicopathological factors were analyzed. Results: Patients with high SUVmax had significantly worse overall survival than those with low SUVmax (49.2% vs. 90.0%, p<0.0001). Multivariate analysis identified high SUVmax as an independent predictor of poor overall survival (hazard ratio=3.16, 95% confidence interval=1.42-7.03, p=0.0048). Furthermore, recurrence-free survival was significantly worse in the high SUVmax group (33.4% vs. 60.4%, p=0.0006). High SUVmax was strongly associated with microscopic vascular invasion (MVI). Combining SUVmax with alpha-fetoprotein improved preoperative prediction of MVI. Conclusion: Preoperative FDG-PET SUVmax is an independent prognostic biomarker in HCC >3 cm and, when combined with serum alpha-fetoprotein, may enhance prediction of MVI. These findings support incorporating metabolic imaging into preoperative risk models to guide management.

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