Outcome prediction in metabolic dysfunction‐associated steatotic liver disease using stain‐free digital pathological assessment

Abstract

AbstractComputational quantification reduces observer‐related variability in histological assessment of metabolic dysfunction‐associated steatotic liver disease (MASLD). We undertook stain‐free imaging using the SteatoSITE resource to generate tools directly predictive of clinical outcomes. Unstained liver biopsy sections (n = 452) were imaged using second‐harmonic generation/two‐photon excitation fluorescence (TPEF) microscopy, and all‐cause mortality and hepatic decompensation indices constructed. The mortality index had greater predictive power for all‐cause mortality (index >.14 vs. </=.14, HR 4.49, p = .003) than the non‐alcoholic steatohepatitis‐Clinical Research Network (NASH‐CRN) (hazard ratio (HR) 3.41, 95% confidence intervals (CI) 1.43–8.15, p = .003) and qFibrosis stage (HR 3.07, 95% CI 1.30–7.26, p = .007). The decompensation index had greater predictive power for decompensation events (index >.31 vs. </=.31, HR 5.96, p < .001) than the NASH‐CRN (HR 3.65, 95% CI 1.81–7.35, p < .001) or qFibrosis stage (HR 3.59, 95% CI 1.79–7.20, p < .001). These tools directly predict hard endpoints in MASLD, without relying on ordinal fibrosis scores as a surrogate, and demonstrate predictive value at least equivalent to traditional or computational ordinal fibrosis scores.