Dataset Information

Whole-liver histogram and texture analysis on T1 maps improves the risk stratification of advanced fibrosis in NAFLD.

ABSTRACT:

Objectives

To assess whole-liver texture analysis on T1 maps for risk stratification of advanced fibrosis in patients with suspected nonalcoholic fatty liver disease (NAFLD).

Methods

This retrospective study included 53 patients. Histogram and texture parameters (volume, mean, SD, median, 5th percentile, 95th percentile, skewness, kurtosis, diff-entropy, diff-variance, contrast, and entropy) of T1 maps were calculated based on the semi-automatically segmented whole-liver volume. A two-step approach combining the Nonalcoholic Fatty Liver Disease Fibrosis Score (NFS) and Fibrosis-4 Index (FIB-4) with the liver stiffness measurement (LSM) for the risk stratification was used. Univariate analysis was performed to identify significant parameters. Logistic regression models were then run on the significant features. Diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis.

Results

In total, 33 (62%) subjects had a low risk and 20 (38%) subjects had an intermediate-to-high risk of advanced fibrosis. The following significantly different parameters with the best performance were diff-entropy, entropy, and diff-variance, with AUROC 0.837 (95% CI 0.73-0.95), 0.821 (95% CI 0.71-0.94), and 0.807 (95% CI 0.69-0.93). The optimal combination of median, 5th percentile, and diff-entropy as a multivariate model improved the diagnostic performance to diagnose an intermediate-to-high risk of advanced fibrosis with AUROC 0.902(95% CI 0.79-0.97).

Conclusions

Parameters obtained by histogram and texture analysis of T1 maps may be a noninvasive analytical approach for stratifying the risk of advanced fibrosis in NAFLD.

Key points

• Variable flip angle (VFA) T1 mapping can be used to acquire 3D T1 maps within a clinically acceptable duration. • Whole-liver histogram and texture parameters on T1 maps in patients with NAFLD can distinguish those with an intermediate-to-high risk of advanced fibrosis. • The multivariate model of combination of texture parameters improved the diagnostic performance for a high risk of advanced fibrosis and clinical parameters offer no added value to the multivariate model.

SUBMITTER: Xu X

PROVIDER: S-EPMC7880972 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Publications

Whole-liver histogram and texture analysis on T1 maps improves the risk stratification of advanced fibrosis in NAFLD.

Xu Xinxin X Zhu Hong H Li Ruokun R Lin Huimin H Grimm Robert R Fu Caixia C Yan Fuhua F

European radiology 20200908 3

<h4>Objectives</h4>To assess whole-liver texture analysis on T1 maps for risk stratification of advanced fibrosis in patients with suspected nonalcoholic fatty liver disease (NAFLD).<h4>Methods</h4>This retrospective study included 53 patients. Histogram and texture parameters (volume, mean, SD, median, 5th percentile, 95th percentile, skewness, kurtosis, diff-entropy, diff-variance, contrast, and entropy) of T1 maps were calculated based on the semi-automatically segmented whole-liver volume. A ...[more]

PMID: 32897416

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Project description:The presence of advanced fibrosis is an important measure of the severity of chronic liver disease. Prior works that have examined the gut microbiome as a novel biomarker for advanced fibrosis have only examined patients with nonalcoholic fatty liver disease. Therefore, our goal was to examine the gut microbiome across varying etiologies of liver disease to create a predictive model for liver fibrosis based upon a microbial signature. Stool samples were obtained from patients with chronic liver disease (n = 50) undergoing FibroScan (ultrasound elastography) at the VA Greater Los Angeles Healthcare System. Healthy control patients (n = 25) were also recruited as a reference population. Fecal samples underwent 16S ribosomal RNA sequencing. Using differentially abundant microbes, a random forest classifier model was created to distinguish advanced fibrosis from mild/moderate fibrosis. The findings were then validated in a separate cohort of chronic liver disease patients (n = 37). Etiologies for liver disease included non-alcoholic liver disease (58.0%), hepatitis C (26.0%), hepatitis B (10.0%), and alcohol (6.0%). Microbiome composition was distinct in liver patients with advanced fibrosis compared to those with minimal fibrosis and healthy controls (p = 0.003). In multivariate negative binomial modeling, 26 bacterial taxa were differentially abundant in patients with advanced fibrosis as compared to those with minimal/moderate fibrosis (q-value < 0.05). A random forests classifier based on these taxa had an AUROC of 0.90 to predict advanced fibrosis. Prevotella copri, which was enriched in patients with advanced fibrosis, was the most strongly predictive microbe in the classifier. The classifier had an AUROC of 0.82 for advanced fibrosis in the validation cohort and Prevotella copri remained the strongest predictive microbe for advanced fibrosis. There is a distinct microbial signature for patients with advanced fibrosis independent of liver disease etiology and other comorbidities. These results suggest that microbial profiles can be used as a non-invasive marker for advanced fibrosis and support the hypothesis that microbes and their metabolites contribute to hepatic fibrosis.

Project description:Background & aimsCurrently available non-invasive tests, including fibrosis-4 index (FIB-4) and liver stiffness measurement (LSM by VCTE), are highly effective at excluding advanced fibrosis (AF) (F ≥3) or cirrhosis in people with non-alcoholic fatty liver disease (NAFLD), but only have moderate ability to rule-in these conditions. Our objective was to develop and validate two new scores (Agile 4 and Agile 3+) to identify cirrhosis or AF, respectively, with optimized positive predictive value and fewer indeterminate results, in individuals with NAFLD attending liver clinics.MethodsThis international study included seven adult cohorts with suspected NAFLD who underwent liver biopsy, LSM and blood sampling during routine clinical practice or screening for trials. The population was randomly divided into a training set and an internal validation set, on which the best-fitting logistic regression model was built, and performance and goodness of fit were assessed, respectively. Furthermore, both scores were externally validated on two large cohorts. Cut-offs for high sensitivity and specificity were derived in the training set to rule-out and rule-in cirrhosis or AF and then tested in the validation set and compared to FIB-4 and LSM.ResultsEach score combined LSM, AST/ALT ratio, platelets, sex and diabetes status, as well as age for Agile 3+. Calibration plots for Agile 4 and Agile 3+ indicated satisfactory to excellent goodness of fit. Agile 4 and Agile 3+ outperformed FIB-4 and LSM in terms of AUROC, percentage of patients with indeterminate results and positive predictive value to rule-in cirrhosis or AF.ConclusionsThe two novel non-invasive scores improve identification of cirrhosis or AF among individuals with NAFLD attending liver clinics and reduce the need for liver biopsy in this population.Impact and implicationsNon-invasive tests currently used to identify patients with advanced fibrosis or cirrhosis, such as fibrosis-4 index and liver stiffness measurement by vibration-controlled transient elastography, have high negative predictive values but high false positive rates, while results are indeterminate for a large number of cases. This study provides scores that will help the clinician diagnose advanced fibrosis or cirrhosis. These new easy-to-implement scores will help liver specialists to better identify (1) patients who need more intensive follow-up, (2) patients who should be referred for inclusion in therapeutic trials, and (3) which patients should be treated with pharmacological agents when effective therapies are approved.

Dataset Information

Whole-liver histogram and texture analysis on T1 maps improves the risk stratification of advanced fibrosis in NAFLD.

Objectives

Methods

Results

Conclusions

Key points

Publications

Whole-liver histogram and texture analysis on T1 maps improves the risk stratification of advanced fibrosis in NAFLD.

Similar Datasets

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