AI-based digital pathology provides newer insights into lifestyle intervention-induced fibrosis regression in MASLD: An exploratory study.
Hai-Yang YuanXiao-Fei TongYa-Yun RenYang-Yang LiXin-Lei WangLi-Li ChenSui-Dan ChenXiao-Zhi JinXiao-Dong WangGiovanni TargherChristopher D ByrneLai WeiVincent W-S WongDean TaiArun J SanyalHong YouMing-Hua ZhengPublished in: Liver international : official journal of the International Association for the Study of the Liver (2024)
Using 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.
Keyphrases
- clinical trial
- end stage renal disease
- high glucose
- metabolic syndrome
- diabetic rats
- double blind
- randomized controlled trial
- chronic kidney disease
- physical activity
- cardiovascular disease
- newly diagnosed
- weight loss
- liver fibrosis
- type diabetes
- artificial intelligence
- drug induced
- prognostic factors
- pet ct
- patient reported outcomes
- open label
- deep learning
- phase ii
- placebo controlled