Digital pathology with AI offers new insights into fibrosis regression in NASH

Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy with artificial intelligence (AI) analyses reveals novel facets of treatment-induced fibrosis regression in nonalcoholic steatohepatitis (NASH), which are not normally captured by existing staging systems, reports a study.

“SHG/TPEF microscopy with AI analyses provides standardized evaluation of NASH features, especially liver fibrosis and collagen fiber quantitation on a continuous scale,” the researchers said. “This approach was applied to gain in-depth understanding of fibrosis dynamics after treatment with tropifexor (TXR), a nonbile acid farnesoid X receptor agonist in patients participating in the FLIGHT-FXR study.”

Ninety-nine patients with NASH (fibrosis stage F2 or F3) who received placebo (n=34), TXR 140 μg (n=37), or TXR 200 μg (n=28) for 48 weeks underwent a total of 198 liver biopsies (paired: baseline and end-of-treatment). Unstained sections from these biopsies were then examined.

The researchers used SHG/TPEF microscopy to quantify liver fibrosis, hepatic fat, and ballooned hepatocytes. They also quantitatively assessed changes in septa morphology, collagen fibre parameters, and zonal distribution within liver lobules.

Unlike conventional microscopy, digital analyses revealed treatment-related reductions in overall liver fibrosis and noticeable regression in perisinusoidal fibrosis in patients with F2 or F3 fibrosis at baseline. [J Hepatol 2022;77:1399-1409]

On concomitant zonal quantitation of fibrosis and steatosis, patients with greater hepatic fat reduction also showed the greatest reduction in perisinusoidal fibrosis. Finally, the researchers noted regressive changes in septa morphology and decrease in septa parameters almost exclusively in F3 patients, who were deemed “unchanged” with conventional scoring.

Overall, this study showed that using digital pathology with SHG/TPEF-derived results provided two novel features in NASH.

“[F]irstly, it provides a new understanding of treatment-induced fibrosis regression starting with marked reduction in perisinusoidal fibrosis in response to decreased fat and lipotoxic drivers in hepatocytes that subsequently extends to portal fibrosis,” the researchers said.

“[S]econdly, it demonstrates the advantages of AI digital pathology by revealing antifibrotic effects of TXR which were not captured by the NASH clinical research network scoring system and conventional microscopy,” they added.

Digital methods

Previous studies have also used SHG/TPEF microscopy to examine fibrosis changes in hepatitis B and nonalcoholic fatty liver disease. [J Hepatol 2014;61:260-269; Hepatology 2020;71:1953-1966; Sci Rep 2018;8:2989; Gut 2020;69:1116-1126; Clin Mol Hepatol 2021;27:44-57]

Apart from SHG/TPEF microscopy, other digital methodologies have been developed, requiring stained slides. These approaches assessed predefined NASH features with supervised or semi-supervised machine-learning models. [Clin Mol Hepatol 2021;27:44-57; Ann Diagn Pathol 2020;47151518; Lab Invest 2020;100:147-160; Metabolism 2021;117154707]

“Collectively, these studies emphasize the innovation and qualities that AI digital pathology brings to investigations of liver diseases, particularly NASH,” the researchers said.

“Indeed, AI digital pathology is expected to be integrated into the workflow of diagnostic and research histopathology in line with the general trend toward the increasing use of AI in medicine,” they added. [Hepatology 2020;72:2000-2013; Clin Res Hepatol Gastroenterol 2020;44:1-3; J Clin Pathol 2021;74:448-455; J Hepatol 2019;70:1016-1018]

“Liver fibrosis is a key prognostic determinant for clinical outcomes in NASH. Current scoring systems have limitations, especially in assessing fibrosis regression,” the researchers noted.