Digital Pathology-Enabled Artificial Intelligence for Fibrosis Assessment in Metabolic Dysfunction-Associated...

Metabolic dysfunction-associated steatotic liver disease (MASLD) is emerging as the most common chronic liver disorder worldwide. However, most of the candidate drugs, except resmetirom, have failed in the metabolic dysfunction-associated steatohepatitis (MASH) trials. The diagnosis of MASH relies on the assessment of key histological features: steatosis, ballooning, lobular inflammation, and fibrosis. Additionally, the current regulatory guidelines consider histological scores as the essential surrogate for the entry and efficacy endpoint decisions in phase IIb and phase III clinical drug trials. The reproducibility of histological scoring systems is increasingly being questioned in clinical trials. Inconsistencies in histopathological reporting, reflected by low inter-observer agreement, are largely attributable to the lack of standardized definitions, ordinal scoring structure with inadequate number of categories, use of variable criteria, ill-defined terminologies, and subjectivity in interpretation, which are further influenced by pre-analytical lab factors. These limitations result in high screen failure rates, poor risk stratification, increased placebo response, and reduced study power in drug trials, making this a serious concern. Liver pathologists are well aware of the urgent need to harmonize definitions and terminology and to transition from semi-quantitative to quantitative measurements using digital pathology (DP) integrated with computer vision and artificial intelligence (AI). Furthermore, standardized, objective, reproducible, and reliable histology-based ground truth is essential for the development and validation of non-invasive diagnostic tests for MASH. Several deep learning (DL) models and architectures, trained on stained or unstained digitized tissue slides using supervised and unsupervised approaches, have been developed, tested, and validated in MASH clinical studies. Liver fibrosis, the strongest primary prognostic determinant of clinical outcomes, has been the most extensively studied histologic feature in MASH. The AI models have demonstrated superior performance with respect to reproducibility and granularity, quantification of fibrosis progression and regression, improved confidence and concordance in Pathologists' scoring, standardization of consensus-based scoring systems, validation of AI-based tools to assist pathologists, and prediction of clinical outcomes. However, several challenges remain in the implementation of DP-enabled AI models in MASLD clinical practice and drug trials. These include ethical and legal considerations, model explainability, patient data privacy and confidentiality, financial investment, and regulatory approvals required for clinical adoption. Nevertheless, digital pathology-coupled DL models have the potential to augment current histopathological assessment in MASH.