Quantification of portal–bridging fibrosis area more accurately reflects fibrosis stage and liver stiffness than whole fibrosis or perisinusoidal fibrosis areas in chronic hepatitis C

Sandrini, J.; Boursier, Jérôme; Chaigneau, Julien; Stürm, Nathalie; Zarski, Jean–Pierre; Bail, Brigitte Le; Lédinghen, Victor de; Calès, Paul; Rousselet, Marie‐Christine

doi:10.1038/modpathol.2013.225

Cited by 20 publications

(16 citation statements)

References 40 publications

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“…A classical limiting factor is thresholding for black-and-white binarization but, in our technique, thresholding was automated. [1011] We observed that cleaning of artefacts had nonstatistically significant effect on morphometric scores, but they induced a large individual variation in about 1 out 20 patients. Since this technical prerequisite is useful, a further study aimed at the automated detection of artefacts is ongoing.…”

Section: Discussionmentioning

confidence: 86%

“…[11] Images of the new morphometric descriptors were validated qualitatively by experts and quantitatively by correlations with other fibrosis descriptors and accuracies for pathological diagnoses or liver stiffness. Other validation steps are needed: Other reference pathologists, relationship with other staging systems like Ishak's staging, other population and clinical outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…Only fibrosis pixels were taken into account, thanks to the preprocessing algorithm and the optional manual cleaning step that deleted artefacts or inappropriate structures. Second, this graded-intensity green image was thresholded according to an automated process:[1011] a fuzzy generalized classification process allowed for the merging of pixel intensities into three classes (fibrosis, steatosis or healthy tissue) using the minimization of an original energy function. This produced a binary black-and-white image where fibrosis appeared in black, and all other structures appeared in white.…”

Section: Methodsmentioning

confidence: 99%

“…Perisinusoidal fibrosis was defined as fibrosis around hepatocytes and around small centrilobular veins; details are provided in Supplement and in a previous study. [11] Area of perisinusoidal fibrosis was easily measured by the difference between the areas of whole fibrosis and of porto-septal fibrosis.…”

Section: Methodsmentioning

confidence: 99%

“…We thus measured the area and fractal dimension of porto-septal fibrosis, the area and fractal dimension of perisinusoidal fibrosis, the area of lobular perisinusoidal fibrosis and the ratio of perisinusoidal fibrosis area among the whole fibrosis; details on fibrosis metrics are available elsewhere. [11] The automated mask detection of porto-septal fibrosis and the new descriptors described were measured at magnification ×5 since they do not need high-resolution images (details in the Supplement).…”

Section: Methodsmentioning

confidence: 99%

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

Calès

Chaigneau²,

Hunault³

et al. 2015

Journal of Pathology Informatics

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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 (rs = 0.835) than METAVIR F staging (rs = 0.756, P < 0.001) and the same correlations with fibrosis biomarkers, e.g., serum hyaluronate: rs = 0.484 versus rs = 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.”

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Section: Discussionmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

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

Calès

Chaigneau²,

Hunault³

et al. 2015

Journal of Pathology Informatics

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A single blood test adjusted for different liver fibrosis targets improves fibrosis staging and especially cirrhosis diagnosis

Calès

Boursier

Oberti

et al. 2018

Hepatology Communications

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Fibrosis blood tests are usually developed using significant fibrosis, which is a unique diagnostic target; however, these tests are employed for other diagnostic targets, such as cirrhosis. We aimed to improve fibrosis staging accuracy by simultaneously targeting biomarkers for several diagnostic targets. A total of 3,809 patients were included, comprising 1,012 individuals with chronic hepatitis C (CHC) into a derivation population and 2,797 individuals into validation populations of different etiologies (CHC, chronic hepatitis B, human immunodeficiency virus/CHC, nonalcoholic fatty liver disease, alcohol) using Metavir fibrosis stages as reference. FibroMeter biomarkers were targeted for different fibrosis‐stage combinations into classical scores by logistic regression. Independent scores were combined into a single score reflecting Metavir stages by linear regression and called Multi‐FibroMeter Version Second Generation (V2G). The primary objective was to combine the advantages of a test targeted for significant fibrosis (FibroMeterV2G) with those of a test targeted for cirrhosis (CirrhoMeterV2G). In the derivation CHC population, we first compared Multi‐FibroMeterV2G to FibroMeterV2G and observed significant increases in the cirrhosis area under the receiver operating characteristic curve (AUROC), Obuchowski index (reflecting all fibrosis‐stage AUROCs), and classification metric (six classes expressed as a correctly classified percentage) and a nonsignificant increase in significant fibrosis AUROC. Thereafter, we compared it to CirroMeterV2G and observed a nonsignificant increase in the cirrhosis AUROC. In all 3,809 patients, respective accuracies for Multi‐FibroMeterV2G and FibroMeterV2G were the following: cirrhosis AUROC, 0.906 versus 0.878 (P < 0.001; versus CirroMeterV2G, 0.897, P = 0.014); Obuchowski index, 0.795 versus 0.791 (P = 0.059); classification, 86.0% versus 82.1% (P < 0.001); significant fibrosis AUROC, 0.833 versus 0.832 (P = 0.366). Multi‐FibroMeterV2G had the highest correlation with the area of portoseptal fibrosis and the highest reproducibility over time. Correct classification rates of Multi‐FibroMeter with hyaluronate (V2G, 86.0%) or without (V3G, 86.1%) did not differ (P = 0.938). Conclusion: Multitargeting biomarkers significantly improves fibrosis staging and especially cirrhosis diagnosis compared to classical single‐targeted blood tests. (Hepatology Communications 2018;2:455‐466)

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Hepatic Fibrosis in Hepatitis C

Sheiko

Rosen

2016

Hepatitis C Virus II

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Quantification of portal–bridging fibrosis area more accurately reflects fibrosis stage and liver stiffness than whole fibrosis or perisinusoidal fibrosis areas in chronic hepatitis C

Cited by 20 publications

References 40 publications

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

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

A single blood test adjusted for different liver fibrosis targets improves fibrosis staging and especially cirrhosis diagnosis

Hepatic Fibrosis in Hepatitis C

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