| Autor: |
Fedaa Najdawi, Kathleen Sucipto, Pratik Mistry, Stephanie Hennek, Christina Jayson, Mary Lin, Darren Fahy, Shawn Kinsey, Ilan Wapinski, Andrew H. Beck, Murray B. Resnick, Archit Khosla, Michael G. Drage |
| Rok vydání: |
2022 |
| Předmět: |
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| DOI: |
10.1101/2022.04.28.22274339 |
| Popis: |
Ulcerative colitis (UC) is a chronic inflammatory bowel disease that is characterized by a relapsing and remitting course. Appropriate assessment of disease activity is critical for adequate treatment decisions. In addition to endoscopic mucosal healing, histologic remission is emerging as a treatment target and a key factor in the evaluation of disease activity and therapeutic efficacy. However, there is no standardized definition of histologic remission, limiting the utility of histologic scoring, and manual pathologist evaluation is subject to intra-and inter-observer variability. Machine learning approaches are increasingly being developed to aid pathologists in accurate and reproducible scoring of histology, and can enable sensitive assessment of clinically relevant features. Here we report a proof-of-concept study using the PathAI platform to develop ML models for identification and quantification of UC histological features directly from hematoxylin and eosin (H&E)-stained whole slide images. Model-predicted histological features were used to quantify tissue area proportions and cell count proportions and densities, which correlated with disease severity and pathologist-assigned Nancy Histological Index (NHI) scores. Moreover, using multivariate analysis based on selected model-predicted histological features, we were able to accurately predict NHI scores, with a weighted kappa (k=0.93) and Spearman correlation (ρ=0.93, p |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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