Liver Biopsies in MASH - Live Panel Discussion at EASL 2024
Date: 5 June 204
About the Webcast
This panel discussion. featuring leading experts in hepatology, focuses on the latest advancements in the diagnosis and treatment of Metabolic Dysfunction-Associated Steatohepatitis (MASH). The discussion covers the recent FDA approval of the first MASH treatment, the evolving role of liver biopsies, and the groundbreaking applications of artificial intelligence and digital pathology in MASH management. Panelists explore how AI-powered analysis of liver biopsies is providing unprecedented insights into disease progression and treatment responses, potentially surpassing traditional histological assessments. They also address the challenges and opportunities in using non-invasive tests versus liver biopsies, the future of combination therapies, and the potential for precision medicine in MASH. The experts emphasize the importance of integrating AI-derived histological features with clinical outcomes and molecular profiling to drive more precise and effective treatment strategies, highlighting the multidisciplinary nature of advancing MASH management.
Topics covered:
- The evolving role of liver biopsies in clinical management and drug trials
- How AI and digital pathology are enhancing our understanding of MASH progression
- The potential of AI to personalize treatment options and improve patient outcomes
- Challenges and opportunities in using non-invasive tests (NITs) vs. liver biopsies
- The future of combination therapies and precision medicine in MASH
Paradigm Shift in MASH Treatment: The FDA approval of Madrigal’s Thyroid Hormone Receptor (THR-β) Agonist marks a significant milestone, offering the first approved treatment for MASH patients.
AI-Powered Histological Analysis: Advanced AI techniques, such as second harmonic generation (SHG) imaging, are providing unprecedented insights into liver fibrosis patterns and disease progression that surpass traditional histological assessments.
Quantitative Fibrosis Assessment: AI-based analysis allows for a more granular, continuous scale of fibrosis assessment (0-100), moving beyond the limitations of the traditional F0-F4 staging system.
Predictive Modeling: AI analysis of liver biopsies shows promise in predicting clinical outcomes, potentially outperforming conventional histological scoring methods.
Combination Therapy Insights: Digital pathology offers unique capabilities in assessing the impact of combination therapies, helping to differentiate effects on fibrosis, inflammation, and metabolic factors.
Non-Invasive Tests (NITs) vs. Biopsies: While NITs are becoming increasingly important, liver biopsies analyzed with AI still play a crucial role, especially in cases of discordant NIT results or for precise disease characterization.
Regulatory Acceptance: The FDA is showing increased openness to AI-based histological analysis in clinical trials, particularly in Phase 2 studies, signaling a potential shift in future drug development paradigms.
Precision Medicine Potential: AI-powered analysis of liver biopsies, combined with molecular profiling, could pave the way for more personalized treatment approaches in MASH.
Challenges in Longitudinal Assessment: Both NITs and repeat biopsies face challenges in accurately measuring disease progression or treatment response, highlighting the need for improved methodologies.
Biopsy Quality Considerations: The length and quality of liver biopsies significantly impact the reliability of both traditional and AI-based analyses, with a minimum length of about 15-16mm suggested for optimal assessment.
Spatial Heterogeneity: AI analysis reveals the importance of considering the spatial distribution of fibrosis and other histological features within the liver, which may have implications for sampling and disease characterization.
Integration with Clinical Outcomes: Ongoing research aims to correlate AI-derived histological features directly with clinical outcomes, potentially enhancing prognostic capabilities.
Expanding Applications: While current focus is on MASH, similar AI-powered approaches show promise for other liver diseases, such as alcohol-related liver disease (ALD) and MetALD.
Multidisciplinary Collaboration: The future of MASH management lies in the integration of AI-powered pathology, molecular profiling, and clinical expertise to drive more precise and effective treatment strategies.