Digital pathology: accurate technique for quantitative assessment of histological features in metabolic‐associated fatty liver disease

Martí-Aguado, David; Rodríguez-Ortega, Alejandro; Mestre‐Alagarda, Claudia; Bauza, Mónica; Valero-Pérez, Elena; Alfaro-Cervelló, Clara; Benlloch, Salvador; Pérez‐Rojas, Judith; Ferrández, Antonio; Alemany-Monraval, Pilar; Escudero-García, Desamparados; Montón, Cristina; Aguilera, Victoria; Serra, Miguel; Martí‐Bonmatí, Luis

doi:10.1111/apt.16100

Cited by 25 publications

(24 citation statements)

References 65 publications

(89 reference statements)

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“…The study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki and received ethical approval by the Institution Review Board of the three participating hospitals (2016/209; 2017/0031/PI, and 29/2019). A portion of the included participant data ( n = 78) has already been published regarding technical developments of digital pathology [ 17 ]. The prior study used a simpler methodology for digital image analysis and did not evaluate other chronic hepatitis etiologies beyond NAFLD.…”

Section: Methodsmentioning

confidence: 99%

“…The histopathological features of participants are summarized in Table 2. Median biopsy length was 20 (17)(18)(19)(20)(21)(22)(23) mm and included 10 (8-13) portal tracts. Histological review showed that 23% had mild steatosis (S1, n = 36), 22% had moderate steatosis (S2, n = 34), and 22% presented severe steatosis (S3, n = 35).…”

Section: Patients and Histological Characteristicsmentioning

confidence: 99%

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Digital Pathology Enables Automated and Quantitative Assessment of Inflammatory Activity in Patients with Chronic Liver Disease

et al. 2021

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Traditional histological evaluation for grading liver disease severity is based on subjective and semi-quantitative scores. We examined the relationship between digital pathology analysis and corresponding scoring systems for the assessment of hepatic necroinflammatory activity. A prospective, multicenter study including 156 patients with chronic liver disease (74% nonalcoholic fatty liver disease-NAFLD, 26% chronic hepatitis-CH etiologies) was performed. Inflammation was graded according to the Nonalcoholic Steatohepatitis (NASH) Clinical Research Network system and METAVIR score. Whole-slide digital image analysis based on quantitative (I-score: inflammation ratio) and morphometric (C-score: proportionate area of staining intensities clusters) measurements were independently performed. Our data show that I-scores and C-scores increase with inflammation grades (p < 0.001). High correlation was seen for CH (ρ = 0.85–0.88), but only moderate for NAFLD (ρ = 0.5–0.53). I-score (p = 0.008) and C-score (p = 0.002) were higher for CH than NAFLD. Our MATLAB algorithm performed better than QuPath software for the diagnosis of low-moderate inflammation (p < 0.05). C-score AUC for classifying NASH was 0.75 (95%CI, 0.65–0.84) and for moderate/severe CH was 0.99 (95%CI, 0.97–1.00). Digital pathology measurements increased with fibrosis stages (p < 0.001). In conclusion, quantitative and morphometric metrics of inflammatory burden obtained by digital pathology correlate well with pathologists’ scores, showing a higher accuracy for the evaluation of CH than NAFLD.

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Section: Methodsmentioning

confidence: 99%

Section: Patients and Histological Characteristicsmentioning

confidence: 99%

Digital Pathology Enables Automated and Quantitative Assessment of Inflammatory Activity in Patients with Chronic Liver Disease

et al. 2021

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“… 76 Indeed, digital pathology is likely to dramatically enhance the value of the liver biopsy in the coming years. 77 , 78 …”

Section: Current and Future Roles Of Liver Histologymentioning

confidence: 99%

“…76 Indeed, digital pathology is likely to dramatically enhance the value of the liver biopsy in the coming years. 77,78 Recent advances in slide scanners now allow the creation of "digital/virtual" slides that allow whole slide imaging (WSI) and additional techniques, such as combining multiphoton microscopy (MPM) with advanced clearing and fluorescent stains (Clearing Histology with MultiPhoton Microscopy (CHiMP)) or use of matrix assisted laser desorption/ionization (MALDI) mass spectrometry. 79 The combination of expert evaluation, digital pathology and artificial intelligence is likely to further enhance the value of histopathology.…”

Section: Researchmentioning

confidence: 99%

The Need for Alternatives to Liver Biopsies: Non-Invasive Analytics and Diagnostics

Neuberger

Cain

2021

HMER

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Histology remains essential for the diagnosis and management of many disorders affecting the liver. However, the biopsy procedure itself is associated with a low risk of harm to the patient and cost to the health services; samples may not be adequate and are subject to sampling variation. Furthermore, interpretation often depends on the skill of the pathologist. Increasingly, new techniques are becoming available that are altering the indications for liver biopsy. Many diseases of the liver can be diagnosed and managed using serological and radiological techniques; the degree of fibrosis and fat can often be assessed by serological or imaging techniques and the nature of space occupying lesions defined by serology, imaging and use of liquid biopsy. However, these techniques, too, are subject to limitations: sensitivity and specificity is not always adequate for diagnosis or management; some techniques are expensive and often also require expert interpretation. Although there may be less need for liver biopsy today, histology remains the gold standard as well as an essential tool for the diagnosis and management of many conditions, especially where there are multiple pathologies, or where a diagnosis cannot or has not been made by alternative approaches. Until less invasive techniques become more reliable and accessible, liver histology will remain a key investigation.

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“…Algorithms in arti cial intelligence (AI) may resolve issues with interrater variability [54], measurement uncertainty and error by providing quantitative assessments of tissue histology and other tangential tasks which may be of immediate value [55,12,56,57,58,59]. A combination of both rater uncertainty and incomplete information about the histology may present additional challenges for training and evaluating AI technologies.…”

Section: Opportunitiesmentioning

confidence: 99%

Bridge Category Models: Development of Bayesian Modelling Procedures to Account for Bridge Ordinal Ratings for Disease Staging

Levy

Bobak

Azizgolshani

et al. 2021

Preprint

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Disease grading and staging is accomplished through the assignment of an ordinal rating. Bridge ratings occur when a rater assigns two adjacent categories. Most statistical methodology necessitates the use of a single ordinal category. Consequently, bridge ratings often go unreported in clinical research studies. We propose three methodologies (Expanded, Mixture, and Collapsed) Bridge Category Models, to account for bridge ratings. We perform simulations to examine the impact of our approaches on detecting treatment effects, and comment on a real-world scenario of staging liver biopsies. Results indicate that if bridge ratings are not accounted for, disease staging models may exhibit significant bias and precision loss. All models worked well when they corresponded to the data generating mechanism.

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Digital pathology: accurate technique for quantitative assessment of histological features in metabolic‐associated fatty liver disease

Cited by 25 publications

References 65 publications

Digital Pathology Enables Automated and Quantitative Assessment of Inflammatory Activity in Patients with Chronic Liver Disease

Digital Pathology Enables Automated and Quantitative Assessment of Inflammatory Activity in Patients with Chronic Liver Disease

The Need for Alternatives to Liver Biopsies: Non-Invasive Analytics and Diagnostics

Bridge Category Models: Development of Bayesian Modelling Procedures to Account for Bridge Ordinal Ratings for Disease Staging

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