ArticleEuropean journal of nuclear medicine and molecular imaging2026
Preoperative ⁶⁸Ga-FAPI-04 PET/CT-derived liver fibrosis quantification independently predicts post-hepatectomy liver failure: a histologically validated study.
Article in European journal of nuclear medicine and molecular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeAccurate preoperative risk stratification for post-hepatectomy liver failure (PHLF) is crucial. This study aimed to investigate whether, in patients deemed eligible for safe resection by conventional clinical criteria, ⁶⁸Ga-FAPI-04 PET/CT-based quantitative assessment of liver fibrosis could serve as a non-invasive predictor for PHLF.
methodsIn this retrospective study, 107 patients with liver cancer underwent preoperative ⁶⁸Ga-FAPI-04 PET/CT. Quantitative parameters, including liver SUVmax, SUVmean, SUVpeak, and liver-to-aorta SUV ratios (TBRs), were measured. These imaging metrics were correlated with histopathological fibrosis stage (S0-S4) and analyzed alongside clinical and surgical variables to identify independent predictors of PHLF.
resultsPHLF occurred in 30 (28.0%) patients. All PET parameters significantly increased with advancing fibrosis stage (all P < 0.05). Multivariable logistic regression identified TBRmax (odds ratio [OR] = 4.590, 95% CI 1.439 to 14.637, P = 0.010), prothrombin time (OR = 1.566, 95% CI 1.009 to 2.428, P = 0.045), and major hepatectomy (OR = 2.857, 95% CI 1.065 to 7.665, P = 0.037) as independent predictors of PHLF. A nomogram incorporating these three variables demonstrated good predictive performance, with an area under the curve of 0.780, satisfactory calibration, and positive net benefit on decision curve analysis.
conclusionPreoperative ⁶⁸Ga-FAPI-04 PET/CT offers a histologically confirmed, non-invasive method to quantify liver fibrosis. This hypothesis-generating model offers a promising approach for preoperative risk stratification, warranting further validation in independent cohorts.
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