The fibrosis-4 cut-off value for significant fibrosis is dependent on the type of non-alcoholic fatty liver disease patients

0.001), HCV infection (OR = 8.83 95% CI = 4.79-16.30, P < 0.001), Gr C (OR = 6.94, 95% CI = 2.65-18.15, P < 0.001), and age (OR = 1.08, 95% CI = 1.05-1.11, P < 0.001) were significant factors for predicting the subjects with advanced fibrosis. Conclusion: Lean MAFLD Taiwanese subjects carrying metabolic abnormalities had a higher chance of advanced fibrosis than others in a community-based setting. FRI119 Optical analysis of liver magnetic resonance images (MRI) 3 T to detect steatohepatitis features: the program DeMILI 3.0. Background and aims: DeMILI in MRI 1.5 T has been proved as useful to detect steatohepatitis in patients with non-alcoholic fatty liver disease (NAFLD) (Gallego-Durán et al. Sci Rep 2016). The aim of this study was to evaluate the potential of optical processing methods applied to non-enhanced contrast liver 3 T MRI to predict steatohe-patitis in NAFLD patients. Method: Seventy-nine biopsy-proven NAFLD patients recruited between October 2019 and December 2021 at 'Virgen del Rocío' University Hospital (HUVR, n = 68) and Valladolid Clinical University Hospital (HCUV, n = 11). Main characteristics: 50.6 % female, 21.5% <45 years old and 32.9% >65 years old, 55.6% showed steatohepatitis in the liver biopsy. 3.0 T MRI was conducted using a whole-body Philips® scanner without contrast medium. 3 different sequences were performed in axial plane: SSFSE-T2 (Single Shot Fast Spin Echo T2-weighted), FAST-STIR (Fast Short inversion Time Inversion Recovery) and DYNAMIC. The entire liver was imaged and 6 consecutive slices, per each sequence, were manually selected covering the whole organ. 84 physical and mathematical feature descriptors (estimators) were computed from every image. Principal Component Analysis was performed to extract the main estimators related to steatohepatitis and two logistic regressions (LR) were built: The first one (DeMILI 3.0-v1) used estimators E63, E12 and E21, n = 43 HUVR patients for training, and n = 36 patients (25 HUVR + 11 HCUV) for validation. The second LR (DeMILI 3.0-v2) was built with estimators E70, E12 and E21 and the same cohort in different subsets: training n = 64 from HUVR, and validation n = 15 (4 HUVR + 11 HCUV). Receiver operating characteristic (ROC) curves were obtained. Results: ROC curves for DeMILI 3.0-v1 and DeMILI 3.0-v2 are shown in Figure 1. The area under the ROC curves (AUROC) obtained were: i) for DeMILI 3.0-v1, AUROC = 0.7378 for training set and AUROC = 0.7307 for validation set, and ii) for DeMILI 3.0-v2, AUROC = 0.7958 for training set and AUROC = 0.6071 for validation set. Figure: ROC curves of LRs (v1 and v2) for training and validation subsets. Conclusion: Initial results with limited cohorts show that DeMILI 3.0 detects steatohepatitis in patients with NAFLD with a diagnosis accuracy of 0.73 (DeMILI 3.0-v1). Validation cohorts include patients from other center than training. DeMILI 3.0-v1 yields results close to DeMILI using 1.5 T MRI. FRI120 The fibrosis-4 cutoff value for significant fibrosis is dependent on the type of non-alcoholic fatty liver disease patients

Publication year:2022

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