Incidence and Predictors of Advanced Liver Fibrosis by a Validated Serum Biomarker in Liver Transplant Recipients

Clinical value of [18F]AlF-NOTA-FAPI-04 PET/CT for assessing early-stage liver fibrosis in adult liver transplantation recipients compared with chronic HBV patients

AIMS

To investigate the clinical value and performance of [18F]AlF-NOTA-FAPI-04 PET/CT in assessing early-stage liver fibrosis in liver transplantation (LT) recipients.

METHODS

A prospective study including 17 LT recipients and 12 chronic Hepatitis B (CHB) patients was conducted. All patients received liver biopsy, transient elastography (TE), and [18F]AlF-NOTA-FAPI-04 PET/CT. On [18F]AlF-NOTA-FAPI-04 PET/CT scans, the liver parenchyma's maximum standardized uptake values (SUVmax) were measured. The receiver operating characteristic (ROC) curve analysis was applied to determine the diagnostic efficacy of [18F]AlF-NOTA-FAPI-04 PET/CT in early-stage liver fibrosis (S1-S2) compared with the diagnostic performance of TE.

RESULTS

Among those 29 patients enrolled in this study, 15(51.7%) had fibrosis S0, 10(34.5%) had S1, and 4(13.8%) had S2, respectively. The SUVmax of patients with early-stage liver fibrosis was significantly higher than those without liver fibrosis in LT recipients and CHB patients (P = 0.004, P = 0.02). In LT recipients, a SUVmax cut-off value of 2.0 detected early-stage liver fibrosis with an AUROC of 0.92 (P = 0.006), and a liver stiffness measurements (LSM) score cut-off value of 8.2 kPa diagnosed early-stage liver fibrosis with an AUROC of 0.80 (P = 0.012). In CHB patients, a SUVmax cut-off value of 2.7 detected early-stage liver fibrosis with an AUROC of 0.94 (P < 0.001) and an LSM scores cut-off value of 8.4 kPa diagnosed early-stage liver fibrosis with an AUROC of 0.91 (P < 0.001).

CONCLUSION

[18F]AlF-NOTA-FAPI-04 PET/CT could be applied to evaluate early-stage liver fibrosis in LT recipients and CHB patients properly, with the potential additional advantages in monitoring and predicting complications after LT.

Development of a Radiomics-Based Model to Predict Graft Fibrosis in Liver Transplant Recipients: A Pilot Study

Liver Transplantation is complicated by recurrent fibrosis in 40% of recipients. We evaluated the ability of clinical and radiomic features to flag patients at risk of developing future graft fibrosis. CT scans of 254 patients at 3-6 months post-liver transplant were retrospectively analyzed. Volumetric radiomic features were extracted from the portal phase using an Artificial Intelligence-based tool (PyRadiomics). The primary endpoint was clinically significant (≥F2) graft fibrosis. A 10-fold cross-validated LASSO model using clinical and radiomic features was developed. In total, 75 patients (29.5%) developed ≥F2 fibrosis by a median of 19 (4.3-121.8) months. The maximum liver attenuation at the venous phase (a radiomic feature reflecting venous perfusion), primary etiology, donor/recipient age, recurrence of disease, brain-dead donor, tacrolimus use at 3 months, and APRI score at 3 months were predictive of ≥F2 fibrosis. The combination of radiomics and the clinical features increased the AUC to 0.811 from 0.793 for the clinical-only model (p = 0.008) and from 0.664 for the radiomics-only model (p < 0.001) to predict future ≥F2 fibrosis. This pilot study exploring the role of radiomics demonstrates that the addition of radiomic features in a clinical model increased the model's performance. Further studies are required to investigate the generalizability of this experimental tool.

The molecular phenotypes of injury, steatohepatitis, and fibrosis in liver transplant biopsies in the INTERLIVER study

To extend previous molecular analyses of rejection in liver transplant biopsies in the INTERLIVER study (ClinicalTrials.gov #NCT03193151), the present study aimed to define the gene expression selective for parenchymal injury, fibrosis, and steatohepatitis. We analyzed genome-wide microarray measurements from 337 liver transplant biopsies from 13 centers. We examined expression of genes previously annotated as increased in injury and fibrosis using principal component analysis (PCA). PC1 reflected parenchymal injury and related inflammation in the early posttransplant period, slowly regressing over many months. PC2 separated early injury from late fibrosis. Positive PC3 identified a distinct mildly inflamed state correlating with histologic steatohepatitis. Injury PCs correlated with liver function and histologic abnormalities. A classifier trained on histologic steatohepatitis predicted histologic steatohepatitis with cross-validated AUC = 0.83, and was associated with pathways reflecting metabolic abnormalities distinct from fibrosis. PC2 predicted histologic fibrosis (AUC = 0.80), as did a molecular fibrosis classifier (AUC = 0.74). The fibrosis classifier correlated with matrix remodeling pathways with minimal overlap with those selective for steatohepatitis, although some biopsies had both. Genome-wide assessment of liver transplant biopsies can not only detect molecular changes induced by rejection but also those correlating with parenchymal injury, steatohepatitis, and fibrosis, offering potential insights into disease mechanisms for primary diseases.