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Poster

Comparison of manual vs machine learning approaches to liver biopsy scoring for NASH and fibrosis: a post hoc analysis of the FALCON 1 study

AASLD 2021

Study Background


  • While manual histological evaluation of liver biopsy tissue is the gold-standard method for fibrosis and disease staging in nonalcoholic steatohepatitis (NASH),1 it is limited by inter- and intra-reader variability

  • Machine learning models that have been trained to analyze and interpret liver
    histopathology may help improve reproducibility of NASH grading and staging2

  • In liver biopsy tissue, fibrosis staging and nonalcoholic fatty liver disease activity score (NAS) results determined by PathAI, a machine learning-based approach, have been shown to correlate with those obtained from manual interpretation2

  • This exploratory post hoc analysis compared manual (single central reader) and PathAI pathology scoring of liver biopsy samples from patients with NASH and stage3 fibrosis in the phase 2b FALCON 1 study


References:

1. Chalasani N, et al. Hepatology. 2018;67(1):328–357.
2. Pokkalla H, et al. AASLD 2019. Abstract 187.
3. Abdelmalek MF, et al. Contemp Clin Trials. 2021;104:106335
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Authors

Shevell et al.