Diagnosis of significant liver fibrosis in patients with chronic hepatitis B using a deep learning-based data integration network

Simultaneous grading diagnosis of liver fibrosis, inflammation, and steatosis using multimodal quantitative ultrasound and artificial intelligence framework

Noninvasive, accurate, and simultaneous grading of liver fibrosis, inflammation, and steatosis is valuable for reversing the progression and improving the prognosis quality of chronic liver diseases (CLDs). In this study, we established an artificial intelligence framework for simultaneous grading diagnosis of these three pathological types through fusing multimodal tissue characterization parameters dug by quantitative ultrasound methods derived from ultrasound radiofrequency signals, B-mode images, shear wave elastography images, and clinical ultrasound systems, using the liver biopsy results as the classification criteria. One hundred forty-two patients diagnosed with CLD were enrolled in this study. The results show that for the classification of fibrosis grade ≥ F1, ≥ F2, ≥ F3, and F4, the highest AUCs were respectively 0.69, 0.82, 0.84, and 0.88 with single clinical indicator alone, and were 0.81, 0.83, 0.89, and 0.91 with the proposed method. For the classification of inflammation grade ≥ A2 and A3, the highest AUCs were respectively 0.66 and 0.76 with single clinical indicator alone and were 0.80 and 0.93 with the proposed method. For the classification of steatosis grade ≥ S1 and ≥ S2, the highest AUCs were respectively 0.71 and 0.90 with single clinical indicator alone and were 0.75 and 0.92 with the proposed method. The proposed method can effectively improve the grading diagnosis performance compared with the present clinical indicators and has potential applications for noninvasive, accurate, and simultaneous diagnosis of CLDs.

US-based Sequential Algorithm Integrating an AI Model for Advanced Liver Fibrosis Screening

Background Noninvasive tests can be used to screen patients with chronic liver disease for advanced liver fibrosis; however, the use of single tests may not be adequate. Purpose To construct sequential clinical algorithms that include a US deep learning (DL) model and compare their ability to predict advanced liver fibrosis with that of other noninvasive tests. Materials and Methods This retrospective study included adult patients with a history of chronic liver disease or unexplained abnormal liver function test results who underwent B-mode US of the liver between January 2014 and September 2022 at three health care facilities. A US-based DL network (FIB-Net) was trained on US images to predict whether the shear-wave elastography (SWE) value was 8.7 kPa or higher, indicative of advanced fibrosis. In the internal and external test sets, a two-step algorithm (Two-step#1) using the Fibrosis-4 Index (FIB-4) followed by FIB-Net and a three-step algorithm (Three-step#1) using FIB-4 followed by FIB-Net and SWE were used to simulate screening scenarios where liver stiffness measurements were not or were available, respectively. Measures of diagnostic accuracy were calculated using liver biopsy as the reference standard and compared between FIB-4, SWE, FIB-Net, and European Association for the Study of the Liver guidelines (ie, FIB-4 followed by SWE), along with sequential algorithms. Results The training, validation, and test data sets included 3067 (median age, 42 years [IQR, 33-53 years]; 2083 male), 1599 (median age, 41 years [IQR, 33-51 years]; 1124 male), and 1228 (median age, 44 years [IQR, 33-55 years]; 741 male) patients, respectively. FIB-Net obtained a noninferior specificity with a margin of 5% (P < .001) compared with SWE (80% vs 82%). The Two-step#1 algorithm showed higher specificity and positive predictive value (PPV) than FIB-4 (specificity, 79% vs 57%; PPV, 44% vs 32%) while reducing unnecessary referrals by 42%. The Three-step#1 algorithm had higher specificity and PPV compared with European Association for the Study of the Liver guidelines (specificity, 94% vs 88%; PPV, 73% vs 64%) while reducing unnecessary referrals by 35%. Conclusion A sequential algorithm combining FIB-4 and a US DL model showed higher diagnostic accuracy and improved referral management for all-cause advanced liver fibrosis compared with FIB-4 or the DL model alone. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Ghosh in this issue.

Automatic Diagnosis of Significant Liver Fibrosis From Ultrasound B-Mode Images Using a Handcrafted-Feature-Assisted Deep Convolutional Neural Network

The accurate diagnosis of significant liver fibrosis ( ≥ F2) in patients with chronic liver disease (CLD) is critical, as ≥ F2 is a crucial factor that should be considered in selecting an antiviral therapy for these patients. This article proposes a handcrafted-feature-assisted deep convolutional neural network (HFA-DCNN) that helps radiologists automatically and accurately diagnose significant liver fibrosis from ultrasound (US) brightness (B)-mode images. The HFA-DCNN model has three main branches: one for automatic region of interest (ROI) segmentation in the US images, another for attention deep feature learning from the segmented ROI, and the third for handcrafted feature extraction. The attention deep learning features and handcrafted features are fused in the back end of the model to enable more accurate diagnosis of significant liver fibrosis. The usefulness and effectiveness of the proposed model were validated on a dataset built upon 321 CLD patients with liver fibrosis stages confirmed by pathological evaluations. In a fivefold cross validation (FFCV), the proposed model achieves accuracy, sensitivity, specificity, and area under the receiver-operating-characteristic (ROC) curve (AUC) values of 0.863 (95% confidence interval (CI) 0.820-0.899), 0.879 (95% CI 0.823-0.920), 0.872 (95% CI 0.800-0.925), and 0.925 (95% CI 0.891-0.952), which are significantly better than those obtained by the comparative methods. Given its excellent performance, the proposed HFA-DCNN model can serve as a promising tool for the noninvasive and accurate diagnosis of significant liver fibrosis in CLD patients.

A Hierarchical Siamese Network for Noninvasive Staging of Liver Fibrosis Based on US Image Pairs of the Liver and Spleen

Due to the heterogeneity of ultrasound (US) images and the indeterminate US texture of liver fibrosis (LF), automatic evaluation of LF based on US images is still challenging. Thus, this study aimed to propose a hierarchical Siamese network that combines the information from liver and spleen US images to improve the accuracy of LF grading. There were two stages in the proposed method. In stage one, a dual-channel Siamese network was trained to extract features from paired liver and spleen patches that were cropped from US images to avoid vascular interferences. Subsequently, the L1 distance was used to quantify the liver-spleen differences (LSDs). In stage two, the pretrained weights from stage one were transferred into the Siamese feature extractor of the LF staging model, and a classifier was trained using the fusion of the liver and LSD features for LF staging. This study was retrospectively conducted on US images of 286 patients with histologically proven liver fibrosis stages. Our method achieved a precision and sensitivity of 93.92% and 91.65%, respectively, for cirrhosis (S4) diagnosis, which is about 8% higher than that of the baseline model. The accuracy of the advanced fibrosis (≥S3) diagnosis and the multi-staging of fibrosis (≤S2 vs. S3 vs. S4) both improved about 5% to reach 90.40% and 83.93%, respectively. This study proposed a novel method that combined hepatic and splenic US images and improved the accuracy of LF staging, which indicates the great potential of liver-spleen texture comparison in noninvasive assessment of LF based on US images.

miR-488-5p mitigates hepatic stellate cell activation and hepatic fibrosis via suppressing TET3 expression

BACKGROUND AND AIMS

Numerous studies have demonstrated that hepatic fibrosis, a progressive condition as an endpoint of multiple chronic hepatic diseases, is largely characterized with the extensive activation of hepatic stellate cells (HSCs). The precise effect of miR-488-5p in HSCs during hepatic fibrosis has not been elucidated.

METHODS

In our study, qRT-PCR was applied to assess the level of miR-488-5p in activated HSCs stimulated by TGF-β1. We built murine liver fibrosis models with carbon tetrachloride (CCl4), high-fat diet (HFD) and bile duct ligation (BDL). In vitro, the effects of miR-488-5p in HSCs were examined through cell proliferation assay and apoptosis. Luciferase reporter assay was applied to identify the underlying target of miR-488-5p. In vivo, the effects of miR-488-5p were explored through mouse liver fibrosis models.

RESULTS

The reduction of miR-488-5p in the activated HSCs induced by TGF-β1 and three mouse hepatic fibrosis models were identified. The in vitro functional experimentations verified that miR-488-5p restrained expression of fibrosis-related markers and proliferative capacity in HSCs. Mechanically, we identified that miR-488-5p inhibited tet methylcytosine dioxygenase 3 (TET3) expression via straightly binding onto the 3' UTR of its mRNA, which sequentially restrained the TGF-β/Smad2/3 pathway. TET3 inhibition induced by the overexpression of miR-488-5p reduced extracellular matrix deposition, which contributed to mitigating mouse liver fibrosis.

CONCLUSION

We highlight that miR-488-5p restrains the activation of HSCs and hepatic fibrosis via targeting TET3 which is involved in the TGF-β/Smad2/3 signaling pathway. Collectively, miR-488-5p is identified as a potential therapeutic target for hepatic fibrosis.