Comparison of diffusion-weighted imaging and MR elastography in staging liver fibrosis: a meta-analysis

Noninvasive diagnosis of liver cirrhosis: qualitative and quantitative imaging biomarkers

A diagnosis of cirrhosis initiates a shift in the management of chronic liver disease and affects the diagnostic workflow and treatment decision of primary liver cancer. Liver biopsy remains the gold standard for cirrhosis diagnosis, but it is invasive and susceptible to sampling bias and observer variability. Various qualitative and quantitative imaging biomarkers based on ultrasound, CT and MRI have been proposed for noninvasive diagnosis of cirrhosis. Qualitative imaging features are easy to apply but have moderate diagnostic sensitivity. Elastography techniques allow quantitative assessment of liver stiffness and are highly accurate for cirrhosis diagnosis. Ultrasound elastography are widely used in clinical practice, while MR elastography has narrower availability. Although not applicable in clinical practice yet, other quantitative imaging features, including liver surface nodularity, linear and volumetric measurement, extracellular volume fraction, liver enhancement on hepatobiliary phase, and parameters derived from diffusion-weighted imaging, can provide additional information of liver morphology, perfusion, and function, thus may increase diagnosis performance. The introduction of radiomics and deep learning has further improved diagnostic accuracy while reducing subjectivity. Several imaging features may also help to assess liver function and outcomes in patients with cirrhosis. In this review, we summarize the qualitative and quantitative imaging biomarkers for noninvasive cirrhosis diagnosis, and the assessment of liver function and outcomes, and discuss the challenges and future directions in this field.

Clinical Application of Quantitative MR Imaging in Nonalcoholic Fatty Liver Disease

Viral hepatitis was previously the most common cause of chronic liver disease. However, in recent years, nonalcoholic fatty liver disease (NAFLD) cases have been increasing, especially in developed countries. NAFLD is histologically characterized by fat, fibrosis, and inflammation in the liver, eventually leading to cirrhosis and hepatocellular carcinoma. Although biopsy is the gold standard for the assessment of the liver parenchyma, quantitative evaluation methods, such as ultrasound, CT, and MRI, have been reported to have good diagnostic performances. The quantification of liver fat, fibrosis, and inflammation is expected to be clinically useful in terms of the prognosis, early intervention, and treatment response for the management of NAFLD. The aim of this review was to discuss the basics and prospects of MRI-based tissue quantifications of the liver, mainly focusing on proton density fat fraction for the quantification of fat deposition, MR elastography for the quantification of fibrosis, and multifrequency MR elastography for the evaluation of inflammation.

Rethinking Liver Fibrosis Staging in Patients with Hepatocellular Carcinoma: New Insights from a Large Two-Center Cohort Study

Background

Hepatocellular carcinoma (HCC) is a prevalent and aggressive malignancy closely related to background chronic liver disease. This study aimed to explore predictive factors associated with background liver fibrosis burden in patients with HCC and sought to construct a practical predictive model for clinical use.

Methods

This large two-center retrospective cohort study evaluated data from Chinese medical centers. Uni- and multivariate ordinal logistic regression analyses were performed to identify variables associated with liver fibrosis stages. Predictive models based on variables identified by multivariate analysis were established in the Derivation Cohort and subjected to internal and external validation. Model performance was evaluated for discriminative and calibration abilities.

Results

Multivariate ordinal logistic regression analysis identified liver fibrosis severity score (LFSS), portal hypertension (PH) severity, plateletcrit (PCT) and model for end-stage liver disease-sodium (MELD-Na) as independent predictors of liver fibrosis stage in HCC patients. Nomograms that integrated these factors disclosed that the area under receiver operating characteristic curves (AUROCs) to predict S1 in the Derivation and External Validation cohorts were 0.850 and 0.919, respectively. Internal validation disclosed C-indexes of 0.823 and 0.833 in the Derivation and External Validation cohorts, respectively, indicating that the nomogram had good and excellent performance for distinguishing between S1 and non-S1 patients. Nomogram performance in the Derivation and External Validation cohorts, respectively, was fair and good to predict stage S2 (AUROCs 0.726, 0.806; C-indexes 0.713, 0.791); poor for S3 (AUROCs 0.648, 0.698; C-indexes 0.616, 0.666); good for S4 (AUROCs 0.812, 0.824; C-indexes 0.804, 0.792); and good for S3+S4 (AUROCs 0.806, 0.840; C-indexes 0.795, 0.811).

Conclusion

We propose new predictive models for the staging of background liver fibrosis in patients with HCC that can be implemented into clinical practice as important complements to hepatic imaging to inform HCC management strategy.

Effect of Iron Deposition on Native T1 Mapping and Blood Oxygen Level Dependent for the Assessment of Liver Fibrosis in Rabbits With Carbon Tetrachloride Intoxication

RATIONALE AND OBJECTIVES

This study aimed to explore the effect of iron deposition on native T1 mapping and blood oxygen level-dependent (BOLD) imaging in detecting liver fibrosis (LF) in a rabbit model.

MATERIALS AND METHODS

An LF group (n = 100) was established by subcutaneously injecting 50% carbon tetrachloride (CCl₄) oil solution, and 20 normal rabbits composed a control group. Native T1 mapping and BOLD were performed, and the T1_native and R₂* quantitative parameters were analyzed by receiver operating characteristic (ROC) and multiple logistic regression analyses, with histopathological results and liver iron content (LIC) serving as reference standards.

RESULTS

In total, 18, 17, 16, 18, and 15 rabbits were histopathologically diagnosed with LF stages F0, F1, F2, F3, and F4, respectively. T1_native (r = 0.47), R₂* (r = 0.75) and LIC (r = 0.61) increased with LF stage progression (p < 0.001). Compared to T1_native values, R₂* performed better in diagnosing the LF stage, especially for distinguishing F1-F2 from F3-F4 (AUC = 0.66 vs. 0.91, p = 0.01). Combined with the LIC, both T1_native and R₂* showed improved diagnostic value in comparison to the individual imaging techniques, particularly for diagnosing F0 vs. F1-F2 and F0 vs. F1-F4, with AUC values of 0.90 vs. 0.70 (p = 0.01) and 0.93 vs. 0.77 (p = 0.01) for T1_native + LIC vs. LIC, respectively.

CONCLUSION

BOLD imaging performed better than native T1 mapping in predicting and diagnosing LF stage progression. The decrease in diagnostic accuracy caused by the deposition of liver iron is a potential pitfall in the assessment of LF with BOLD imaging and native T1 mapping.