The emerging role of second harmonic generation/two photon excitation for precision digital analysis of liver fibrosis in MASH clinical trials

Conventional histopathological evaluation of liver biopsy slides has been invaluable in assessing the causes of liver injury, the severity of the underlying disease processes, and the degree of resulting fibrosis. However, the use of conventional histologic assessments as endpoints in clinical trials is limited by the reliability of scoring systems, variability in interpretation of histologic features and translation of continuous variables into categorical scores. To increase the precision and reproducibility of liver biopsy assessment, several artificial intelligence/machine learning (AI/ML) approaches have been developed to analyse high-resolution digital images of liver biopsy specimens. Multiple AI/ML platforms are in development and promising results have been reported in post hoc analyses of clinical trial biopsies. One such technique employs second harmonic generation/two photon excitation (SHG/TPE) microscopy, which is unique in using unstained liver biopsies (avoiding challenges related to staining variability), to provide high-resolution images of collagen fibres, enabling assessment and quantification of collagen morphometry. One SHG/TPE microscopy methodology coupled with AI/ML-based analysis, qFibrosis, has been used post hoc as an exploratory endpoint in several clinical trials for MASH (metabolic dysfunction-associated steatohepatitis), which have demonstrated its ability to provide a consistent and more nuanced assessment of liver fibrosis that still correlates well with traditional staging. In this review, we summarise the development of qFibrosis and outline the need for additional studies to validate it as a sensitive marker of changes in fibrosis in the context of treatment trials and to correlate these changes with subsequent liver-related outcomes.