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Quantitative assessment of liver fibrosis by digital image analysis reveals correlation with qualitative clinical fibrosis staging in liver transplant patients.

ABSTRACT: Technologies for digitizing tissues provide important quantitative data for liver histopathology investigation. We aimed to assess liver fibrosis degree with quantitative morphometric measurements of histopathological sections utilizing digital image analysis (DIA) and to further investigate if a correlation with histopathologic scoring (Scheuer staging) exists. A retrospective study of patients with at least two post-liver transplant biopsies having a Scheuer stage of ? 2 at baseline were gathered. Portal tract fibrotic percentage (%) and size (?m2) were measured by DIA, while clinical fibrosis score was measured by the Scheuer system. Correlations between DIA measurements and Scheuer scores were computed by Spearman correlation analysis. Differences between mean levels of fibrosis (score, size, and percentage) at baseline versus second visit were computed by Student's t-test. P values < 0.05 were considered significant. Of 22 patients who met the study criteria, 54 biopsies were included for analysis. Average levels ±standard error [S.E.] of portal tract fibrotic percentage (%) and size (?m2) progressed from 46.5 ± 3.6% at baseline to 61.8 ± 3.8% at the second visit (P = 0.005 by Student's t-test), and from 28,075 ± 3,232 ?m2 at base line to 67,146 ± 10,639 ?m2 at the second visit (P = 0.002 by Student's t-test), respectively. Average levels of Scheuer fibrosis scores progressed from 0.55±0.19 at baseline to 1.14±0.26 at the second visit (P = 0.02 by Student's t-test). Portal tract fibrotic percentage (%) and portal tract fibrotic size were directly correlated with clinical Scheuer fibrosis stage, with Spearman correlation coefficient and P value computed as r = 0.70, P < 0.0001 and r = 0.41, P = 0.002, respectively. Digital quantitative assessment of portal triad size and fibrosis percentage demonstrates a strong correlation with visually assessed histologic stage of liver fibrosis and complements the standard assessment for allograft monitoring, suggesting the utility of future WSI analysis.

SUBMITTER: Jiang K

PROVIDER: S-EPMC7521727 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Quantitative assessment of liver fibrosis by digital image analysis reveals correlation with qualitative clinical fibrosis staging in liver transplant patients.

Jiang Kun K Mohammad Mohammad K MK Dar Wasim A WA Kong Jun J Farris Alton B AB

PloS one 20200928 9

Technologies for digitizing tissues provide important quantitative data for liver histopathology investigation. We aimed to assess liver fibrosis degree with quantitative morphometric measurements of histopathological sections utilizing digital image analysis (DIA) and to further investigate if a correlation with histopathologic scoring (Scheuer staging) exists. A retrospective study of patients with at least two post-liver transplant biopsies having a Scheuer stage of ≤ 2 at baseline were gathe ...[more]

PMID: 32986732

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Project description:ObjectiveThe objective was to develop a multiparametric CT algorithm to stage liver fibrosis in patients with chronic hepatitis C virus (HCV) infection.Materials and methodsAbdominal CT and laboratory measures in 469 patients with HCV (340 men and 129 women; mean age, 50.1 years) were compared against the histopathologic Metavir fibrosis reference standard (F0, n = 49 patients; F1, n = 69 patients; F2, n = 102 patients; F3, n = 76 patients; F4, n = 173 patients). From the initial candidate pool, nine CT and two laboratory measures were included in the final assessment (CT-based features: hepatosplenic volumetrics, texture features, liver surface nodularity [LSN] score, and linear CT measurements; laboratory-based measures: Fibrosis-4 [FIB-4] score and aspartate transaminase-to-platelets ratio index [APRI]). Univariate logistic regression and multivariate logistic regression were performed with ROC analysis, proportional odds modeling, and probabilities.ResultsROC AUC values for the model combining all 11 parameters for discriminating significant fibrosis (≥ F2), advanced fibrosis (≥ F3), and cirrhosis (F4) were 0.928, 0.956, and 0.972, respectively. For all nine CT-based parameters, these values were 0.905, 0.936, and 0.972, respectively. Using more simplified panels of two, three, or four parameters yielded good diagnostic performance; for example, a two-parameter model combining only LSN score with FIB-4 score had ROC AUC values of 0.886, 0.915, and 0.932, for significant fibrosis, advanced fibrosis, and cirrhosis. The LSN score performed best in the univariate analysis.ConclusionMultiparametric CT assessment of HCV-related liver fibrosis further improves performance over the performance of individual parameters. An abbreviated panel of LSN score and FIB-4 score approached the diagnostic performance of more exhaustive panels. Results of the abbreviated panel compare favorably with elastography, but this approach has the advantage of retrospective assessment using preexisting data without planning.

Project description:Accurate staging of liver fibrosis is crucial to guide therapeutic decisions for patients with nonalcoholic fatty liver disease (NAFLD). Digital image analysis has emerged as a promising tool for quantitative assessment of fibrosis in chronic liver diseases. We sought to determine the relationship of histologic fibrosis stage with fiber amounts quantified in liver biopsy specimens for the better understanding of NAFLD progression. We measured area ratios of collagen and elastin fibers in Elastica van Gieson-stained biopsy tissues from 289 patients with NAFLD from four hospitals using an automated computational method and examined their correlations with Brunt's fibrosis stage. As a secondary analysis, we performed multivariable logistic regression analysis to assess the associations of the combined area ratios of collagen and elastin with noninvasive fibrosis markers. The combined fiber area ratios correlated strongly with Brunt's stage (Spearman correlation coefficient, 0.78; P < 0.0001), but this relationship was nonlinear (P = 0.007) with striking differences between stage 4 (median area ratios, 12.3%) and stages 0-3 (2.1%, 2.8%, 4.3%, and 4.8%, respectively). Elastin accumulation was common in areas of thick bridging fibrosis and thickened venous walls but not in areas of perisinusoidal fibrosis. The highest tertile of the combined fiber area ratios was associated with the fibrosis-4 index and serum type IV collagen 7s domain (7s collagen) levels, whereas the upper two tertiles of the fiber amounts significantly associated with body mass index, aspartate aminotransferase, and 7s collagen in the multivariable analysis. Conclusion: Quantitative fibrosis assessment reveals a nonlinear relationship between fibrosis stage and fiber amount, with a marked difference between stage 4 and stage 3 and much smaller differences among stages 0-3, suggesting a heterogeneity in disease severity within NAFLD-related cirrhosis. (Hepatology Communications 2018;2:58-68).

Project description:Data quality is increasingly recognized as one of the most important confounding factors in brain imaging research. It is particularly important for studies of brain development, where age is systematically related to in-scanner motion and data quality. Prior work has demonstrated that in-scanner head motion biases estimates of structural neuroimaging measures. However, objective measures of data quality are not available for most structural brain images. Here we sought to identify quantitative measures of data quality for T1-weighted volumes, describe how these measures relate to cortical thickness, and delineate how this in turn may bias inference regarding associations with age in youth. Three highly-trained raters provided manual ratings of 1840 raw T1-weighted volumes. These images included a training set of 1065 images from Philadelphia Neurodevelopmental Cohort (PNC), a test set of 533 images from the PNC, as well as an external test set of 242 adults acquired on a different scanner. Manual ratings were compared to automated quality measures provided by the Preprocessed Connectomes Project's Quality Assurance Protocol (QAP), as well as FreeSurfer's Euler number, which summarizes the topological complexity of the reconstructed cortical surface. Results revealed that the Euler number was consistently correlated with manual ratings across samples. Furthermore, the Euler number could be used to identify images scored "unusable" by human raters with a high degree of accuracy (AUC: 0.98-0.99), and out-performed proxy measures from functional timeseries acquired in the same scanning session. The Euler number also was significantly related to cortical thickness in a regionally heterogeneous pattern that was consistent across datasets and replicated prior results. Finally, data quality both inflated and obscured associations with age during adolescence. Taken together, these results indicate that reliable measures of data quality can be automatically derived from T1-weighted volumes, and that failing to control for data quality can systematically bias the results of studies of brain maturation.

Dataset Information

Quantitative assessment of liver fibrosis by digital image analysis reveals correlation with qualitative clinical fibrosis staging in liver transplant patients.

Publications

Quantitative assessment of liver fibrosis by digital image analysis reveals correlation with qualitative clinical fibrosis staging in liver transplant patients.

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