Digital pathology for nonalcoholic steatohepatitis assessment

Sanyal, Arun J.; Jha, Prakash; Kleiner, David E.

doi:10.1038/s41575-023-00843-7

Cited by 7 publications

(1 citation statement)

References 111 publications

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“…In this context, DP coupled with AI-based feature extraction has shown recent exciting results for histopathological diagnoses, particularly in the field of NAFLD, where this workflow may be used to automatically detect, localize, and quantify histological features that could be classified for severity using AI-generated scores. In particular, Sanyal et al [113], in a recent perspective, discussed key positive points and gaps in knowledge for the use of AI-supervised and unsupervised approaches for the detection of NASH-related features and staging of fibrosis in NAFLD/MAFLD.…”

Section: Future Directions Of Ai Approaches In Liver Diseasesmentioning

confidence: 99%

The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review

Grignaffini,

Barbuto,

Troiano

et al. 2024

Diagnostics

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Digital pathology (DP) has begun to play a key role in the evaluation of liver specimens. Recent studies have shown that a workflow that combines DP and artificial intelligence (AI) applied to histopathology has potential value in supporting the diagnosis, treatment evaluation, and prognosis prediction of liver diseases. Here, we provide a systematic review of the use of this workflow in the field of hepatology. Based on the PRISMA 2020 criteria, a search of the PubMed, SCOPUS, and Embase electronic databases was conducted, applying inclusion/exclusion filters. The articles were evaluated by two independent reviewers, who extracted the specifications and objectives of each study, the AI tools used, and the results obtained. From the 266 initial records identified, 25 eligible studies were selected, mainly conducted on human liver tissues. Most of the studies were performed using whole-slide imaging systems for imaging acquisition and applying different machine learning and deep learning methods for image pre-processing, segmentation, feature extractions, and classification. Of note, most of the studies selected demonstrated good performance as classifiers of liver histological images compared to pathologist annotations. Promising results to date bode well for the not-too-distant inclusion of these techniques in clinical practice.

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Section: Future Directions Of Ai Approaches In Liver Diseasesmentioning

confidence: 99%

The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review

Grignaffini,

Barbuto,

Troiano

et al. 2024

Diagnostics

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A roadmap for clinical trials in MASH-related compensated cirrhosis

Pericàs,

Anstee,

Augustin

et al. 2024

Nat Rev Gastroenterol Hepatol

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AI‐based digital pathology provides newer insights into lifestyle intervention‐induced fibrosis regression in MASLD: An exploratory study

Yuan,

Tong,

Ren

et al. 2024

Liver International

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Background and AimsLifestyle intervention is the mainstay of therapy for metabolic dysfunction‐associated steatohepatitis (MASH), and liver fibrosis is a key consequence of MASH that predicts adverse clinical outcomes. The placebo response plays a pivotal role in the outcome of MASH clinical trials. Second harmonic generation/two‐photon excitation fluorescence (SHG/TPEF) microscopy with artificial intelligence analyses can provide an automated quantitative assessment of fibrosis features on a continuous scale called qFibrosis. In this exploratory study, we used this approach to gain insight into the effect of lifestyle intervention‐induced fibrosis changes in MASH.MethodsWe examined unstained sections from paired liver biopsies (baseline and end‐of‐intervention) from MASH individuals who had received either routine lifestyle intervention (RLI) (n = 35) or strengthened lifestyle intervention (SLI) (n = 17). We quantified liver fibrosis with qFibrosis in the portal tract, periportal, transitional, pericentral, and central vein regions.ResultsAbout 20% (7/35) and 65% (11/17) of patients had fibrosis regression in the RLI and SLI groups, respectively. Liver fibrosis tended towards no change or regression after each lifestyle intervention, and this phenomenon was more prominent in the SLI group. SLI‐induced liver fibrosis regression was concentrated in the periportal region.ConclusionUsing digital pathology, we could detect a more pronounced fibrosis regression with SLI, mainly in the periportal region. With changes in fibrosis area in the periportal region, we could differentiate RLI and SLI patients in the placebo group in the MASH clinical trial. Digital pathology provides new insight into lifestyle‐induced fibrosis regression and placebo responses, which is not captured by conventional histological staging.

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Digital pathology for nonalcoholic steatohepatitis assessment

Cited by 7 publications

References 111 publications

The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review

The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review

A roadmap for clinical trials in MASH-related compensated cirrhosis

AI‐based digital pathology provides newer insights into lifestyle intervention‐induced fibrosis regression in MASLD: An exploratory study

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