Nonalcoholic Fatty Liver Disease: Different Classifications Concordance and Relationship between Degrees of Morphological Features and Spectrum of the Disease

Monteiro, Juliana Maya; Monteiro, G; Caroli-Bottino, Adriana; Pannain, Vera Lúcia

doi:10.1155/2014/526979

Cited by 9 publications

(11 citation statements)

References 24 publications

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“…There was excellent concordance between the two scorings for the patients diagnosed NASH and not NASH. Thirty‐one percent of the cases were diagnosed as borderline NASH according to NAS, which is similar to other published studies . However, the borderline group analysis revealed that significant number of cases (88.06%) diagnosed NASH by SAF algorithm were underdiagnosed by NAS.…”

Section: Discussionsupporting

confidence: 87%

“…also found similar relationship between histological grade of steatosis and steatohepatitis . Similarly, lobular inflammation correlated with NASH in other studies .…”

Section: Discussionsupporting

confidence: 85%

“…However, the borderline group analysis revealed that significant number of cases (88.06%) diagnosed NASH by SAF algorithm were underdiagnosed by NAS. In NAS 3–4, histology is not benign and is reported in other studies as well . Thus, NAS 3–4 should be further elaborated with combination of the SAF algorithm and pattern‐based interpretation.…”

Section: Discussionmentioning

confidence: 77%

“…NAS score 4 was more frequently diagnosed as NASH by the SAF system with 94.4% labeled as NASH in comparison with NAS score 3, where only 79.4% were diagnosed NASH by SAF. Other studies have also observed that NAS ≥4 has high sensitivity and specificity for NASH diagnosis .…”

Section: Discussionmentioning

confidence: 83%

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Non‐alcoholic fatty liver disease – histological scoring systems: a large cohort single‐center, evaluation study

Rastogi

Shasthry

Agarwal

et al. 2017

APMIS

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Non-alcoholic fatty liver disease (NAFLD) is an increasingly common cause of chronic liver disease. Till date, liver biopsy remains the gold standard for identification and quantification of the wide histological spectra of NAFLD. Histological scorings are very useful and widely applied for the diagnosis and management in clinical trials and follow-up studies of non-alcoholic steatohepatitis (NASH). However, in view of scarce published literature, there is a need to evaluate them in large cohort of NAFLD. This study was aimed to evaluate the two histological scoring systems (NAS-CRN, SAF) in the diagnosis of NAFLD and to assess the role of histological characteristics as injury markers in NAFLD. Retrospective histological study of liver biopsies of 1000 patients diagnosed as NAFLD, between 2010 and 2016, was conducted. Histopathologic evaluation and semiquantiative scoring based on NAS-CRN and SAF algorithm and their correlation with serum aminotransferase and fibrosis were performed. Liver biopsies were classified according to the NAS-CRN scoring, as NAS <3 (not NASH) in 72 (7.2%), NAS 3-4 (borderline NASH) in 310 (31%), and NAS ≥5 (definite NASH) in 618 (61.8%), and SAF classified 117 (11.7%) not NASH and 883 (88.3%) definite NASH. There was excellent concordance for definite NASH and not NASH; however, 88.06% of borderline NASH was classified as NASH by SAF. 76.39% by NAS and 78.63% by SAF algorithm who were diagnosed as not NASH showed the presence of fibrosis; however, higher stages of fibrosis were significantly more prevalent in definite NASH, excluding burnt-out cirrhosis. Serum ALT was significantly associated with increasing stages of fibrosis (p < 0.001) and the three categories (not NASH, borderline NASH, and definite NASH) when classified as with/without fibrosis (p < 0.001). Steatosis of higher grades, more ballooned cells, and more foci of Lobular Inflammation were found in significantly higher proportion of patients with NASH (p < 0.001), with higher fibrosis stages (p < 0.001) and higher serum ALT levels (p < 0.001). NAFLD classifications based on histological scoring NAS-CRN and SAF algorithm are concordant for the category of definite NASH and not NASH, while borderline NASH shows discrepant interpretation. There was highly significant correlation between the NAS and SAF categories with high grades of histological characteristics, with serum ALT and with higher stages of fibrosis. Exclusion of fibrosis is a limitation with both scores.

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Section: Discussionsupporting

confidence: 87%

“…also found similar relationship between histological grade of steatosis and steatohepatitis . Similarly, lobular inflammation correlated with NASH in other studies .…”

Section: Discussionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 77%

Section: Discussionmentioning

confidence: 83%

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Non‐alcoholic fatty liver disease – histological scoring systems: a large cohort single‐center, evaluation study

Rastogi

Shasthry

Agarwal

et al. 2017

APMIS

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“…Monteiro et al [15] studied the degree of morphological feature and fibrosis grade. Krishnan [26] extracted GLCM and fractal features from segmented ultrasound images and classifies ten types of diffused and focal liver diseases using support vector machine with a classification rate of 92%.…”

Section: Literature Surveymentioning

confidence: 99%

Neural network based texture analysis of CT images for fatty and cirrhosis liver classification

Mala¹,

Sadasivam

Alagappan³

2015

Applied Soft Computing

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Accuracy of controlled attenuation parameter compared with ultrasound for detecting hepatic steatosis in children with severe obesity

et al. 2020

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Objectives To determine the diagnostic accuracy of controlled attenuation parameter (CAP) on FibroScan® in detecting and grading steatosis in a screening setting and perform a head-to-head comparison with conventional B-mode ultrasound. Methods Sixty children with severe obesity (median BMI z-score 3.37; median age 13.7 years) were evaluated. All underwent CAP and US using a standardized scoring system. Magnetic resonance spectroscopy proton density fat fraction (MRS-PDFF) was used as a reference standard. Results Steatosis was present in 36/60 (60%) children. The areas under the ROC (AUROC) of CAP for the detection of grade ≥ S1, ≥ S2, and ≥ S3 steatosis were 0.80 (95% CI: 0.67–0.89), 0.77 (95% CI: 0.65–0.87), and 0.79 (95% CI: 0.66–0.88), respectively. The AUROC of US for the detection of grade ≥ S1 steatosis was 0.68 (95% CI: 0.55–0.80) and not significantly different from that of CAP (p = 0.09). For detecting ≥ S1 steatosis, using the optimal cutoffs, CAP (277 dB/m) and US (US steatosis score ≥ 2) had a sensitivity of 75% and 61% and a specificity of 75% and 71%, respectively. When using echogenicity of liver parenchyma as only the scoring item, US had a sensitivity of 70% and specificity of 46% to detect ≥ S1 steatosis. The difference in specificity of CAP and US when using only echogenicity of liver parenchyma of 29% was significant (p = 0.04). Conclusion The overall performance of CAP is not significantly better than that of US in detecting steatosis in children with obesity, provided that the standardized scoring of US features is applied. When US is based on liver echogenicity only, CAP outperforms US in screening for any steatosis (≥ S1). Key Points • The areas under the ROC curves of CAP and ultrasound (US) for detecting grade ≥ S1 steatosis were 0.80 and 0.68, respectively, and were not significantly different (p = 0.09). • For detecting grade ≥ S1 steatosis in severely obese children, CAP had a sensitivity of 75% and a specificity of 75% at its optimal cutoff value of 277 dB/m. • For detecting grade ≥ S1 steatosis in clinical practice, both CAP and US can be used, provided that the standardized scoring of US images is used.

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Nonalcoholic Fatty Liver Disease: Different Classifications Concordance and Relationship between Degrees of Morphological Features and Spectrum of the Disease

Cited by 9 publications

References 24 publications

Non‐alcoholic fatty liver disease – histological scoring systems: a large cohort single‐center, evaluation study

Non‐alcoholic fatty liver disease – histological scoring systems: a large cohort single‐center, evaluation study

Neural network based texture analysis of CT images for fatty and cirrhosis liver classification

Accuracy of controlled attenuation parameter compared with ultrasound for detecting hepatic steatosis in children with severe obesity

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