ArticleDiagnostics (Basel, Switzerland)2020
An Improved qFibrosis Algorithm for Precise Screening and Enrollment into Non-Alcoholic Steatohepatitis (NASH) Clinical Trials.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- The Asian Pacific association for the study of the liver clinical practice guidelines for the diagnosis and management of metabolic dysfunction-associated fatty liver disease.Hepatology international · 2025Guideline
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- What is new in the 2025 APASL guidelines for metabolic dysfunction-associated fatty liver disease?Hepatobiliary surgery and nutrition · 2026Article
- Liver biopsy in the modern era: from traditional techniques to artificial intelligence and multi-omics integration.Frontiers in medicine · 2025Review
- Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Artificial intelligence-assisted digital pathology for non-alcoholic steatohepatitis: current status and future directions.Journal of hepatology · 2024Review
- Digital pathology for nonalcoholic steatohepatitis assessment.Nature reviews. Gastroenterology & hepatology · 2024Review
- Non-alcoholic fatty liver disease: the pathologist's perspective.Clinical and molecular hepatology · 2023Review
- Artificial intelligence and deep learning: New tools for histopathological diagnosis of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis.Computational and structural biotechnology journal · 2023Review
- [New advances of artificial intelligence in the diagnosis of non-alcoholic fatty liver disease].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2022Review
- Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction.JHEP reports : innovation in hepatology · 2022Review
- Article
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Authors and funding
11 authors at 6 institutions in 3 countries.
Funding
Abstract
backgroundMany clinical trials with potential drug treatment options for non-alcoholic fatty liver disease (NAFLD) are focused on patients with non-alcoholic steatohepatitis (NASH) stages 2 and 3 fibrosis. As the histological features differentiating stage 1 (F1) from stage 2 (F2) NASH fibrosis are subtle, some patients may be wrongly staged by the in-house pathologist and miss the opportunity for enrollment into clinical trials. We hypothesized that our refined artificial intelligence (AI)-based algorithm (qFibrosis) can identify these subtle differences and serve as an assistive tool for in-house pathologists.
methodsLiver tissue from 160 adult patients with biopsy-proven NASH from Singapore General Hospital (SGH) and Peking University People's Hospital (PKUH) were used. A consensus read by two expert hepatopathologists was organized. The refined qFibrosis algorithm incorporated the creation of a periportal region that allowed for the increased detection of periportal fibrosis. Consequently, an additional 28 periportal parameters were added, and 28 pre-existing perisinusoidal parameters had altered definitions.
resultsTwenty-eight parameters (20 periportal and 8 perisinusoidal) were significantly different between the F1 and F2 cases that prompted a change of stage after a careful consensus read. The discriminatory ability of these parameters was further demonstrated in a comparison between the true F1 and true F2 cases as 26 out of the 28 parameters showed significant differences. These 26 parameters constitute a novel sub-algorithm that could accurately stratify F1 and F2 cases.
conclusionThe refined qFibrosis algorithm incorporated 26 novel parameters that showed a good discriminatory ability for NASH fibrosis stage 1 and 2 cases, representing an invaluable assistive tool for in-house pathologists when screening patients for NASH clinical trials.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.