Quantitative fibrosis identifies biliary tract involvement and is associated with outcomes in pediatric autoimmune liver disease

Hepatol Commun. 2024 Dec 11;9(1):e0594. doi: 10.1097/HC9.0000000000000594. eCollection 2025 Jan 1.

Abstract

Background: Children with autoimmune liver disease (AILD) may develop fibrosis-related complications necessitating a liver transplant. We hypothesize that tissue-based analysis of liver fibrosis by second harmonic generation (SHG) microscopy with artificial intelligence analysis can yield prognostic biomarkers in AILD.

Methods: Patients from single-center studies with unstained slides from clinically obtained liver biopsies at AILD diagnosis were identified. Baseline demographics and liver biochemistries at diagnosis and 1 year were collected. Clinical endpoints studied included the presence of varices, variceal bleeding, ascites, HE, and liver transplant. In collaboration with HistoIndex, unstained slides underwent SHG/artificial intelligence analysis to map fibrosis according to 10 quantitative fibrosis parameters based on tissue location, including total, periportal, perisinusoidal, and pericentral area and length of strings.

Results: Sixty-three patients with AIH (51%), primary sclerosing cholangitis (30%), or autoimmune sclerosing cholangitis (19%) at a median of 14 years old (range: 3-24) were included. An unsupervised analysis of quantitative fibrosis parameters representing total and portal fibrosis identified a patient cluster with more primary sclerosing cholangitis/autoimmune sclerosing cholangitis. This group had more fibrosis at diagnosis by METAVIR classification of histopathological review of biopsies (2.5 vs. 2; p = 0.006). This quantitative fibrosis pattern also predicted abnormal 12-month ALT with an OR of 3.6 (1.3-10, p = 0.014), liver complications with an HR of 3.2 (1.3-7.9, p = 0.01), and liver transplantation with an HR of 20.1 (3-135.7, p = 0.002).

Conclusions: The application of SHG/artificial intelligence algorithms in pediatric-onset AILD provides improved insight into liver histopathology through fibrosis mapping. SHG allows objective identification of patients with biliary tract involvement, which may be associated with a higher risk for refractory disease.

MeSH terms

  • Adolescent
  • Artificial Intelligence*
  • Autoimmune Diseases / complications
  • Autoimmune Diseases / pathology
  • Biopsy
  • Child
  • Child, Preschool
  • Cholangitis, Sclerosing* / complications
  • Cholangitis, Sclerosing* / immunology
  • Cholangitis, Sclerosing* / pathology
  • Female
  • Hepatitis, Autoimmune / complications
  • Hepatitis, Autoimmune / pathology
  • Humans
  • Liver / pathology
  • Liver Cirrhosis* / pathology
  • Liver Transplantation*
  • Male
  • Prognosis
  • Young Adult