AI Article Synopsis
- Intra and inter-pathologist variability complicates the evaluation of metabolic dysfunction-associated steatohepatitis (MASH) biopsy results, hindering patient selection and assessment quality in clinical trials.
- A study analyzed 120 histology slides with and without AI assistance to evaluate its impact on pathologists' reliability in fibrosis staging, especially for early fibrosis stages.
- Results showed that AI assistance significantly improved concordance among pathologists, increasing agreement rates for clinical trial inclusion and exclusion, which could enhance the overall efficiency and reliability of MASH-related clinical research.
Article Abstract
Background & Aims: Intra and inter-pathologist variability poses a significant challenge in metabolic dysfunction-associated steatohepatitis (MASH) biopsy evaluation, leading to suboptimal selection of patients and confounded assessment of histological response in clinical trials. We evaluated the utility of an artificial intelligence (AI) digital pathology (DP) platform to aid pathologists improve the reliability of fibrosis staging.
Methods: A total of 120 digitized histology slides from two trials (NCT03517540, NCT03912532) were analysed by four expert hepatopathologists, with and without AI-assistance in a randomized, cross-over design. We utilized the HistoIndex AI DP platform, consisting of unstained second harmonic generation/two photon excitation fluorescence (SHG/TPEF) images and AI quantitative fibrosis (qF) values.
Results: AI-assistance significantly improved inter-pathologist kappa for fibrosis (F)-staging, particularly for early fibrosis (F0-F2), with reduced variance around the median reads. Intra-pathologist kappa was unchanged. AI-assistance increased pathologist concordance for identifying clinical trial inclusion subjects (F2-F3) from 45% to 71%, exclusion subjects (F0/F1/F4) from 38% to 55%, and evaluation of fibrosis response to treatment from 49% to 61%. SHG/TPEF images, qFibrosis continuous values, and qF-stage were considered useful by at least 3 out of 4 pathologists in 83%, 55%, and 38% cases, respectively. In the context of a clinical trial, the increase in inter-pathologist concordance in this study is modeled to result in a ∼25% reduction in the potential need for adjudication as well as a ∼50% increase in the study power.
Conclusions: The use of AI DP enhances inter-rater reliability of fibrosis staging for MASH. This indicates that the SHG/TPEF-based AI DP tool is useful for assisting pathologists in assessing fibrosis, thereby enhancing clinical trial efficiency and reliability of fibrosis readouts in response to treatments.
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| http://dx.doi.org/10.1016/j.jhep.2024.11.032 | DOI Listing |
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