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

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.

FIGURE 1

Clustering of patients according to overall fibrosis and periportal fibrosis by SHG. Unsupervised hierarchical clustering analysis of the 10 qFibrosis variables revealed 3 variable clusters: (1) PctSHG, StrArea, StrAreaPT, and StrLengthPT; (2) StrAreaCV and StrLengthCV; and (3) NumStr, StrLength, StrAreaPS, and StrLengthPS. Two distinct patient clusters (clusters A and B) were observed, where patients from cluster B had greater overall fibrosis (PctSHG and StrArea) and portal tract fibrosis (StrAreaPT and StrLengthPT) than those in cluster A. Abbreviations: NumStr, number of strings; PctSHG, percent SHG; SHG, second harmonic generation; StrArea, string area; StrAreaCV, string area central vein; StrAreaPS, string area perisinusoidal space; StrLengthPS, string length perisinusoidal space; StrAreaPT, string area portal tract; StrLength, string length; StrLengthCV, string length central vein; StrLengthPT, string length portal tract.

FIGURE 2

Patterns of fibrosis by SHG. SHG images of liver biopsies according to AILD diagnosis and Histocluster. Patients in cluster B have evidence of greater overall fibrosis and periportal fibrosis than patients in cluster A. PC1 value is indicated for each biopsy. Abbreviations: AILD, autoimmune liver disease; PC, principal component; SHG, second harmonic generation.

FIGURE 3

SHG/AI analysis of fibrosis and histopathologic staging of fibrosis. The principal component 1 is highly correlated (p < 0.0001) with both Metavir classification (A) and Ishak stage (B) according to Spearman correlation analysis. Abbreviations: AI, artificial intelligence; SHG, second harmonic generation.

FIGURE 4

The PC1 segregates the AILD cohort into 2 clusters. The PC1 is strongly associated with hierarchical clusters A and B (p < 0.0001). The qFibrosis variables that comprised PC1 are the dominant variables directing subject clustering into hierarchical clusters A and B (Figure 1), indicating that overall and portal tract fibrosis may be most relevant in segregating phenotypes. Abbreviations: AILD, autoimmune liver disease; PC1, principal component 1.