Magnetic Resonance Elastography and Diffusion Weighted Imaging in the Evaluation of Hepatic Fibrosis in Chronic Hepatitis B

Hennedige, Tiffany; Wang, Gang; Leung, Fiona P.; Alsaif, Hind S.; Teo, Lynette; Lim, Seng Gee; Wee, Aileen; Venkatesh, Sudhakar K.

doi:10.5009/gnl16079

Cited by 33 publications

(15 citation statements)

References 22 publications

(27 reference statements)

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“…Therefore, radiomic analysis of different MRI sequences may differently perform. In this study, the textural parameters of HPs provided intermediate or high correlation with mean stiffness values (because of differences in stiffness), which have been widely proven by MRE reports to show high diagnostic performance for the classification of fibrosis [31,32]. Compared with the previously cited study reports, our study demonstrated the best diagnostic performance, and these textural features can be considered to be optimal parameters superior to conventional stiffness values.…”

Section: Discussionsupporting

confidence: 49%

Value of radiomic analysis of data from magnetic resonance elastography for diagnosing fibrosis stages in patients with hepatitis B/C

et al. 2019

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Purpose To evaluate the value of radiomic analysis of data from MR elastography (MRE) for diagnosing stages of fibrosis in patients with chronic hepatitis B/C (CHB/CHC). Materials and methods MRE images from 140 CHB/CHC patients with pathologically proven F0-F4 hepatic fibrosis stages were retrospectively analyzed, which is approved by our Institutional Review Board (IRB). A total of 364 radiomic features were extracted from spin-echo echo planar imaging with three-dimensional postprocessing (EPI 3D) and gradient recalled echo with two-dimensional postprocessing (GRE 2D) MRE data, including histogram parameters (HPs), gray level co-occurrence matrix (GLCM), and gray level run length matrix. Feature selection (significance test and correlation analysis) and classification (logistic regression models) were performed to stage ≥ F1, ≥ F2, ≥ F3 and F4, with a leave-one-out-based cross-validation method. Receiver operating characteristic analysis including areas under the curves (AUCs) was performed. Results Univariate analysis showed the best-performing features for classifying fibrosis stages were HPs (AUC range 0.798-0.929 for EPI 3D and 0.727-0.916 for GRE 2D). In multivariate radiomic analysis, AUCs for diagnosing fibrosis of ≥ F1, ≥ F2, ≥ F3 and cirrhosis (F4) were 0.888, 0.934, 0.924 and 0.948 for EPI 3D MRE, and 0.887, 0.867, 0.892 and 0.941 for GRE 2D MRE, which were slightly higher than that of EPI and GRE stiffness for each fibrosis stage (EPI 0.816, 0.924, 0.870 and 0.918; GRE 0.882, 0.800, 0.865 and 0.911). The AUCs of the EPI 3D model were significantly higher than GRE 2D model for staging clinically significant fibrosis (0.934 vs 0.867, P = 0.030) and advanced fibrosis (0.924 vs 0.892, P = 0.029). Data conclusion Radiomic analysis of MRE facilitates fibrosis classification in CHB/CHC patients, especially for identifying intermediate fibrosis, with a higher diagnostic performance by the EPI 3D MRE.

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Section: Discussionsupporting

confidence: 49%

Value of radiomic analysis of data from magnetic resonance elastography for diagnosing fibrosis stages in patients with hepatitis B/C

et al. 2019

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“…These results may be credited to the ability of DDC in capturing a continuous distribution of diffusion coefficients from every diffusion compartment (decided by the “no tissue compartmentalization” assumption)[ 54 , 55 ]. Beside different diffusion techniques, magnetic resonance elastography (MRE) has also been utilized in many studies for LF staging[ 8 , 25 , 56 ]. Although MRE demonstrated excellent diagnostic ability, even greater than DWI or IVIM, it is currently not widely available around the world since it requires special equipment as well as technical expertise for data acquisition and image postprocessing.…”

Section: Discussionmentioning

confidence: 99%

Value of intravoxel incoherent motion in detecting and staging liver fibrosis: A meta-analysis

Zheng

Wei

Chen

et al. 2020

WJG

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BACKGROUND Liver fibrosis (LF) is a common pathological feature of all chronic liver diseases. With the accumulation of extracellular matrix in the fibrotic liver, true molecular water diffusion and perfusion-related diffusion are restricted. Intravoxel incoherent motion (IVIM) can capture the information on tissue diffusivity and microcapillary perfusion separately and reflect the fibrotic severity with diffusion coefficients. AIM To investigate the diagnostic performance of IVIM in detecting and staging LF with histology as a reference standard. METHODS A comprehensive literature search was conducted to identify studies on the diagnostic accuracy of IVIM for assessment of histologically proven LF. The stages of LF were classified as F0 (no fibrosis), F1 (portal fibrosis without septa), F2 (periportal fibrosis with few septa), F3 (septal fibrosis), and F4 (cirrhosis) according to histopathological findings. Data were extracted to calculate the pooled sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio, as well as the area under the summary receiver operating characteristic curve (AUC) in each group. RESULTS A total of 12 studies with 923 subjects were included in this meta-analysis with 5 studies ( n = 465) for LF ≥ F1, 9 studies ( n = 757) for LF ≥ F2, 4 studies ( n = 413) for LF ≥ F3, and 6 studies ( n = 562) for LF = F4. The pooled sensitivity and specificity were estimated to be 0.78 (95% confidence interval: 0.73-0.82) and 0.81 (0.74-0.86) for LF ≥ F1 detection with IVIM; 0.82 (0.79-0.86) and 0.80 (0.75-0.84) for staging F2 fibrosis; 0.85 (0.79-0.90) and 0.83 (0.77-0.87) for staging F3 fibrosis, and 0.90 (0.84-0.94) and 0.75 (0.70-0.79) for detecting F4 cirrhosis, respectively. The AUCs for LF ≥ F1, F2, F3, F4 detection were 0.862 (0.811-0.914), 0.883 (0.856-0.909), 0.886 (0.865-0.907), and 0.899 (0.866-0.932), respectively. Moderate to substantial heterogeneity was observed with inconsistency index ( I 2 ) ranging from 0% to 77.9%. No publication bias was detected. CONCLUSION IVIM is a noninvasive tool with good diagnostic performance in detecting and staging LF. Optimized and standardized IVIM protocols are needed to further improve its diagnostic accuracy in clinical practice.

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“…Nowadays, several new methods including magnetic resonance elastography (MRE), perfusion, and gadoxetic acid-enhanced MRI, are increasingly being used to assess liver fibrosis, especially in predicting the severity of liver fibrosis [7][8][9][10]26]. MRE was more accurate than DWI for the detection and stratification of liver fibrosis in chronic hepatitis B (CHB) patients [27], however, MRE had several confounders such as concomitant liver steatosis, iron deposition, also MRE requires the use of expensive equipment and additional hardware [28,29]. Challenges for perfusion MRI of the liver including breathing motion, lack of standardized measurement, and some significant postprocessing is required for quantitative parameter analysis [26].…”

Section: Discussionmentioning

confidence: 99%

Value of whole-liver apparent diffusion coefficient histogram analysis for quantification of liver fibrosis stages

Yang

Grimm

et al. 2019

Chin J Acad Radiol

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Aim To explore whether volumetric acquisition of histogram analysis of apparent diffusion coefficient (ADC) maps could quantify liver fibrosis stages. Materials and methods 84 patients underwent MRI exams including DWI before surgery or liver biopsy were included. Histogram analysis of whole liver ADC maps were performed and parameters including ADC mean, ADC median, ADC skewness, ADC kurtosis, ADC standard deviation (SD), ADC 5th, 10th, 25th, 75th, 90th and 95th percentiles were calculated. The correlations between different histopathologic grades and histogram analysis parameters were determined. For the significant parameters, receiver operating characteristics (ROC) analyses were further established to evaluate the potential diagnostic performance for differentiating liver fibrosis stages. Results The ADC mean and ADC median, ADC SD, ADC5th, 10th, 25th, 75th, 90th and 95th percentile were significantly lower with the increasing grade of liver fibrosis (rho: from − 0.580 to − 0.230). ADC skewness and ADC kurtosis were significantly higher with the increasing grade of liver fibrosis (rho: 0.243, 0.347, respectively). Areas under the ROC curve (AUC) of ADC mean and ADC 75th percentile for identification liver fibrosis (F1-F4), moderate to advanced liver fibrosis (F2-F4), advanced liver fibrosis (F3-F4), cirrhosis (F4) were 0.87, 0.83, 0.82, 0.76 and 0.88, 0.82, 0.80, 0.74, respectively. Conclusion Histogram analysis of volumetric acquisition of liver ADC maps has a potential value for quantifying liver fibrosis grades.

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Magnetic Resonance Elastography and Diffusion Weighted Imaging in the Evaluation of Hepatic Fibrosis in Chronic Hepatitis B

Cited by 33 publications

References 22 publications

Value of radiomic analysis of data from magnetic resonance elastography for diagnosing fibrosis stages in patients with hepatitis B/C

Value of radiomic analysis of data from magnetic resonance elastography for diagnosing fibrosis stages in patients with hepatitis B/C

Value of intravoxel incoherent motion in detecting and staging liver fibrosis: A meta-analysis

Value of whole-liver apparent diffusion coefficient histogram analysis for quantification of liver fibrosis stages

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