Automated computerized image analysis for the user-independent evaluation of disease severity in preclinical models of NAFLD/NASH

Pathologists use a semiquantitative scoring system (NAS or SAF score) to facilitate the reporting of disease severity and evolution. Similar scores are applied for the same purposes in rodents. Histological scores have inherent inter- and intra-observer variability and yield discrete and not continuous values. Here we performed an automatic numerical quantification of NASH features on liver sections in common preclinical NAFLD/NASH models. High-fat diet-fed foz/foz mice (Foz HF) or wild-type mice (WT HF) known to develop progressive NASH or an uncomplicated steatosis, respectively, and C57Bl6 mice fed a choline-deficient high-fat diet (CDAA) to induce steatohepatitis were analyzed at various time points. Automated software image analysis of steatosis, inflammation, and fibrosis was performed on digital images from entire liver sections. Data obtained were compared with the NAS score, biochemical quantification, and gene expression. As histologically assessed, WT HF mice had normal liver up to week 34 when they harbor mild steatosis with if any, little inflammation. Foz HF mice exhibited grade 2 steatosis as early as week 4, grade 3 steatosis at week 12 up to week 34; inflammation and ballooning increased gradually with time. Automated measurement of steatosis (macrovesicular steatosis area) revealed a strong correlation with steatosis scores (r = 0.89), micro-CT liver density, liver lipid content (r = 0.89), and gene expression of CD36 (r = 0.87). Automatic assessment of the number of F4/80-immunolabelled crown-like structures strongly correlated with conventional inflammatory scores (r = 0.79). In Foz HF mice, collagen deposition, evident at week 20 and progressing at week 34, was automatically quantified on picrosirius red-stained entire liver sections. The automated procedure also faithfully captured and quantitated macrovesicular steatosis, mixed inflammation, and pericellular fibrosis in CDAA-induced steatohepatitis. In conclusion, the automatic numerical analysis represents a promising quantitative method to rapidly monitor NAFLD activity with high-throughput in large preclinical studies and for accurate monitoring of disease evolution.

Characterization of liver changes in ZSF1 rats, an animal model of metabolic syndrome

BACKGROUND

The non-alcoholic fatty liver disease is the hepatic counterpart of the metabolic syndrome. ZSF1 rats are a metabolic syndrome animal model in which liver changes have not been described yet.

AIM

The characterization of liver histological and innate immunity changes in ZSF1 rats.

METHODS

Five groups of rats were included (n = 7 each group): healthy Wistar-Kyoto control rats (Ctrl), hypertensive ZSF1 lean (Ln), ZSF1 obese rats with a normal diet (Ob), ZSF1 obese rates with a high-fat diet (Ob-HFD), and ZSF1 obese rats with low-intensity exercise training (Ob-Ex). The animals were sacrificed at 20 weeks of age, their livers were collected for: a) measurements of the area of steatosis, fibrosis and inflammation (histomorphological analysis); and b) innate immunity (toll-like receptor [TLR] 2, TLR4, peroxisome proliferator-activated receptor γ [PPARγ], toll interacting protein [TOLLIP]) and inflammatory marker (tumor necrosis factor-alpha [TNFα], interleukin 1 [IL-1]) expression analysis by real-time PCR.

RESULTS

Ob, Ob-HFD and Ob-Ex were significantly heavier than Ln and Ctrl animals. Ob, Ob-HFD and Ob-Ex animals had impaired glucose tolerance and insulin resistance. ZSF1 Ob, Ob-HFD and Ob-Ex presented a higher degree of steatosis (3,5x; p < 0.05) than Ctrl or ZSF1 Ln rats. Steatohepatitis and fibrosis were not observed in any of the groups. No differences in expression were observed between Ctrl, Ln and Ob animals (except for the significantly higher expression of TOLLIP observed in the Ob vs Ln comparison). Ob-HFD and Ob-Ex rats showed increased expression of PPARγ and TOLLIP as compared to other groups. However, both groups also showed increased expression of TLR2 and TLR4. Nevertheless, this did not translate into a differential expression of TNFα or IL-1 in any of the groups.

CONCLUSION

The ZSF1 model is associated with liver steatosis but not with steatohepatitis or a significantly increased expression of innate immunity or inflammation markers.

Quantification of liver fat: A comprehensive review

Fat accumulation in the liver causes metabolic diseases such as obesity, hypertension, diabetes or dyslipidemia by affecting insulin resistance, and increasing the risk of cardiac complications and cardiovascular disease mortality. Fatty liver diseases are often reversible in their early stage; therefore, there is a recognized need to detect their presence and to assess its severity to recognize fat-related functional abnormalities in the liver. This is crucial in evaluating living liver donors prior to transplantation because fat content in the liver can change liver regeneration in the recipient and donor. There are several methods to diagnose fatty liver, measure the amount of fat, and to classify and stage liver diseases (e.g. hepatic steatosis, steatohepatitis, fibrosis and cirrhosis): biopsy (the gold-standard procedure), clinical (medical physics based) and image analysis (semi or fully automated approaches). Liver biopsy has many drawbacks: it is invasive, inappropriate for monitoring (i.e., repeated evaluation), and assessment of steatosis is somewhat subjective. Qualitative biomarkers are mostly insufficient for accurate detection since fat has to be quantified by a varying threshold to measure disease severity. Therefore, a quantitative biomarker is required for detection of steatosis, accurate measurement of severity of diseases, clinical decision-making, prognosis and longitudinal monitoring of therapy. This study presents a comprehensive review of both clinical and automated image analysis based approaches to quantify liver fat and evaluate fatty liver diseases from different medical imaging modalities.

Automated morphometry provides accurate and reproducible virtual staging of liver fibrosis in chronic hepatitis C

Background:

Liver fibrosis staging provides prognostic value, although hampered by observer variability. We used digital analysis to develop diagnostic morphometric scores for significant fibrosis, cirrhosis and fibrosis staging in chronic hepatitis C.

Materials and Methods:

We automated the measurement of 44 classical and new morphometric descriptors. The reference was histological METAVIR fibrosis (F) staging (F0 to F4) on liver biopsies. The derivation population included 416 patients and liver biopsies ≥20 mm-length. Two validation population included 438 patients.

Results:

In the derivation population, the area under the receiver operating characteristic (AUROC) for clinically significant fibrosis (F stage ≥2) of a logistic score combining 5 new descriptors (stellar fibrosis area, edge linearity, bridge thickness, bridge number, nodularity) was 0.957. The AUROC for cirrhosis of 6 new descriptors (edge linearity, nodularity, portal stellar fibrosis area, portal distance, granularity, fragmentation) was 0.994. Predicted METAVIR F staging combining 8 morphometric descriptors agreed well with METAVIR F staging by pathologists: κ = 0.868. Morphometric score of clinically significant fibrosis had a higher correlation with porto-septal fibrosis area (r_s = 0.835) than METAVIR F staging (r_s = 0.756, P < 0.001) and the same correlations with fibrosis biomarkers, e.g., serum hyaluronate: r_s = 0.484 versus r_s = 0.476 for METAVIR F (P = 0.862). In the validation population, the AUROCs of clinically significant fibrosis and cirrhosis scores were, respectively: 0.893 and 0.993 in 153 patients (biopsy < 20 mm); 0.955 and 0.994 in 285 patients (biopsy ≥ 20 mm). The three morphometric diagnoses agreed with consensus expert reference as well as or better than diagnoses by first-line pathologists in 285 patients, respectively: significant fibrosis: 0.733 versus 0.733 (κ), cirrhosis: 0.900 versus 0.827, METAVIR F: 0.881 versus 0.865.

Conclusion:

The new automated morphometric scores provide reproducible and accurate diagnoses of fibrosis stages via “virtual expert pathologist.”

Quantification of liver fat content: comparison of triple-echo chemical shift gradient-echo imaging and in vivo proton MR spectroscopy

PURPOSE

To validate a triple-echo gradient-echo sequence for measuring the fat content of the liver, by using hydrogen 1((1)H) magnetic resonance (MR) spectroscopy as the reference standard.

MATERIALS AND METHODS

This prospective study was approved by the appropriate ethics committee, and written informed consent was obtained from all patients. In 37 patients with type 2 diabetes (31 men, six women; mean age, 56 years), 3.0-T single-voxel point-resolved (1)H MR spectroscopy of the liver (Couinaud segment VII) was performed to calculate the liver fat fraction from the water (4.7 ppm) and methylene (1.3 ppm) peaks, corrected for T1 and T2 decay. Liver fat fraction was also computed from triple-echo (consecutive in-phase, opposed-phase, and in-phase echo times) breath-hold spoiled gradient-echo sequence (flip angle, 20 degrees), by estimating T2* and relative signal intensity loss between in- and opposed-phase values, corrected for T2* decay. Pearson correlation coefficient, Bland-Altman 95% limit of agreement, and Lin concordance coefficient were calculated.

RESULTS

Mean fat fractions calculated from the triple-echo sequence and (1)H MR spectroscopy were 10% (range, 0.7%-35.6%) and 9.7% (range, 0.2%-34.1%), respectively. Mean T2* time was 14.7 msec (range, 5.4-25.4 msec). Pearson correlation coefficient was 0.989 (P < .0001) and Lin concordance coefficient was 0.988 (P < .0001). With the Bland-Altman method, all data points were within the limits of agreement.

CONCLUSION

A breath-hold triple-echo gradient-echo sequence with a low flip angle and correction for T2* decay is accurate for quantifying fat in segment VII of the liver. Given its excellent correlation and concordance with (1)H MR spectroscopy, this triple-echo sequence could replace (1)H MR spectroscopy in longitudinal studies.