Michael Pavlides

Background & Aims Digital pathology image analysis can phenotype liver fibrosis using histological traits that reflect collagen content, morphometry and architecture. Here, we aimed to calculate fibrosis severity scores to quantify these traits. Methods Liver biopsy slides were categorised by Ishak stage and aetiology. We used a digital pathology technique to calculate four fibrosis severity scores: Architecture Composite Score (ACS), Collagen Composite Score (CCS), Morphometric Composite Score (MCS) and Phenotypic Fibrosis Composite Score (PH‐FCS). We compared how these scores varied according to disease stage and aetiology. Results We included 80 patients (40% female, mean age 59.0 years, mean collagen proportionate area 17.1%) with mild (F0‐2, n = 28), moderate (F3‐4, n = 17) or severe (F5‐6, n = 35) fibrosis. All four aetiology independent scores corelated with collagen proportionate area (ACS: r p = .512, CCS: r p = .727, MCS: r p = .777, PFCS: r = .772, p < .01 for all) with significant differences between moderate and severe fibrosis ( p < .05). ACS increased primarily between moderate and severe fibrosis (by 95% to 226% depending on underlying aetiology), whereas MCS and CCS accumulation was more varied. We used 28 qFTs that distinguished between autoimmune‐ and alcohol‐related liver disease to generate an MCS that significantly differed between mild and severe fibrosis for these aetiologies ( p < .05). Conclusions We describe four aetiology‐dependent and ‐independent severity scores that quantify fibrosis architecture, collagen content and fibre morphometry. This approach provides additional insight into how progression of architectural changes and accumulation of collagen may differ depending on underlying disease aetiology.

Background & Aims Primary sclerosing cholangitis (PSC) and IgG4‐related sclerosing cholangitis (IgG4‐SC) are chronic fibro‐inflammatory immune‐mediated hepatobiliary conditions that are challenging to distinguish in a clinical setting. Accurate non‐invasive biomarkers for discriminating PSC and IgG4‐SC are important to ensure a correct diagnosis, prompt therapy and adequate cancer surveillance. Methods We performed nuclear magnetic resonance (NMR)‐based metabolomic profiling using serum samples collected prospectively from patients with PSC ( n = 100), IgG4‐SC ( n = 23) and healthy controls (HC; n = 16). Results Multivariate analysis of the serum metabolome discriminated PSC from IgG4‐SC with greater accuracy (AUC 0.95 [95%CI 0.90–0.98]) than IgG4 titre (AUC 0.87 [95%CI 0.79–0.94]). When inflammatory bowel disease (IBD) was excluded as a comorbid condition (IgG4‐SC n = 20, PSC n = 22), the diagnostic AUC increased to 1.0, suggesting that the metabolome differences identified are not a result of the increased prevalence of IBD in PSC relative to IgG4‐SC patients. Serum lactate ( p < .0001), glucose ( p < .01) and glutamine ( p < .01) metabolites were increased in IgG4‐related disease (IgG4‐RD) and IgG4‐SC individuals compared to PSC, whereas mobile choline ( p < .05), 3‐hydroxybutyric acid ( p < .01) and ‐CH 3 lipoprotein resonances ( p < .01) were decreased. Conclusions Taken together, serum metabolomic profiling has the potential to be incorporated as a diagnostic criterion, independent of IgG4 titre, to improve the diagnosis of IgG4‐RD and help distinguish IgG4‐SC from PSC.