Background and Aims: With distinct mechanisms of action, the combination of tropifexor (TXR) and cenicriviroc (CVC) may provide an effective treatment for NASH. This randomized, multicenter, double-blind, phase 2b study assessed the safety and efficacy of TXR and CVC combination, compared with respective monotherapies. Approach and Results: Patients (N = 193) were randomized 1:1:1:1 to once-daily TXR 140 μg (TXR₁₄₀), CVC 150 mg (CVC), TXR 140 μg + CVC 150 mg (TXR₁₄₀ + CVC), or TXR 90 μg + CVC 150 mg (TXR₉₀ + CVC) for 48 weeks. The primary and secondary end points were safety and histological improvement, respectively. Rates of adverse events (AEs) were similar across treatment groups. Pruritus was the most frequently experienced AE, with highest incidence in the TXR₁₄₀ group (40.0%). In TXR and combination groups, alanine aminotransferase (ALT) decreased from baseline to 48 weeks (geometric mean change: −21%, TXR₁₄₀; −16%, TXR₁₄₀ + CVC; −13%, TXR₉₀ + CVC; and +17%, CVC). Reductions in body weight observed at week 24 (mean changes from baseline: TXR₁₄₀, −2.5 kg; TXR₁₄₀ + CVC, −1.7 kg; TXR₉₀ + CVC, −1.0 kg; and CVC, −0.1 kg) were sustained to week 48. At least 1-point improvement in fibrosis stage/steatohepatitis resolution without worsening of fibrosis was observed in 32.3%/25.8%, 31.6%/15.8%, 29.7%/13.5%, and 32.5%/22.5% of patients in the TXR₁₄₀, CVC, TXR₁₄₀ + CVC, and TXR₉₀ + CVC groups, respectively. Conclusions: The safety profile of TXR + CVC combination was similar to respective monotherapies, with no new signals. TXR monotherapy showed sustained ALT and body weight decreases. No substantial incremental efficacy was observed with TXR + CVC combination on ALT, body weight, or in histological end points compared with monotherapy.

Intra and inter-pathologist variability poses a significant challenge in metabolic dysfunction-associated steatohepatitis (MASH) biopsy evaluation, leading to suboptimal selection of patients and confounded assessment of histological response in clinical trials. We evaluated the utility of an artificial intelligence (AI) digital pathology (DP) platform to help pathologists improve the reliability of fibrosis staging. A total of 120 digitized histology slides from two trials (NCT03517540, NCT03912532) were analyzed by four expert hepatopathologists, with and without AI assistance in a randomized, crossover design. We utilized an AI DP platform consisting of unstained second harmonic generation/two photon excitation fluorescence (SHG/TPEF) images and AI quantitative fibrosis (qFibrosis) values. AI assistance significantly improved inter-pathologist kappa for fibrosis staging, particularly for early fibrosis (F0-F2), with reduced variance around the median reads. Intra-pathologist kappa was unchanged. AI assistance increased pathologist concordance for identifying clinical trial inclusion cases (F2-F3) from 45% to 71%, exclusion cases (F0/F1/F4) from 38% to 55%, and evaluation of fibrosis response to treatment from 49% to 61%. SHG/TPEF images, qFibrosis continuous values, and qFibrosis stage were considered useful by at least three out of four pathologists in 83%, 55%, and 38% of cases, respectively. In the context of a clinical trial, the increase in inter-pathologist concordance was modeled to result in a ∼25% reduction in the potential need for adjudication as well as a ∼45% increase in the study power for a kappa improvement from ∼0.4 to ∼0.7. The use of AI DP enhances inter-rater reliability of fibrosis staging for MASH. This indicates that the SHG/TPEF-based AI DP tool is useful for assisting pathologists in assessing fibrosis, thereby enhancing clinical trial efficiency and reliability of fibrosis readouts in response to treatments. Implementing an AI DP platform as a tool for pathologists significantly improved inter-pathologist agreement on fibrosis staging, particularly for early-stage fibrosis (F0-F2), which is critical for clinical trial eligibility. The second harmonic generation imaging technology used in conjunction with AI quantitative scores provided enhanced visualization of fibrosis with an indication of severity along the disease continuum. This led to increased pathologist confidence in fibrosis staging and, therefore, increased pathologist concordance for the classification of clinical trial inclusion/exclusion and evaluation of treatment, compared to a standard scoring method based on traditional stains without AI assistance. Improved pathologist concordance with AI assistance could streamline clinical trial processes, reducing the need for adjudication and enhancing study power, potentially decreasing required sample sizes. Continued exploration of the utility of AI assistance across a broader range of pathologists and in prospective clinical trials will be essential for validating the effectiveness of AI assistance.

This study aimed to understand the dynamic changes in fibrosis and its relationship with the evaluation of post-treatment viral hepatitis using qFibrosis. A total of 158 paired pre- and post-treatment liver samples from patients with chronic hepatitis B (CHB; n = 100) and C (CHC; n = 58) were examined. qFibrosis was employed with artificial intelligence (AI) to analyze the fibrosis dynamics in the portal tract (PT), periportal (PP), midzonal, pericentral, and central vein (CV) regions. All patients with CHB achieved a virological response after 78 weeks of treatment, whereas patients with CHC achieved a sustained viral response after 24 weeks. For patients initially staged as F5/6 (Ishak system) at baseline, the post-treatment cases exhibited a significant reduction in the collagen proportionate area (CPA) (25–69%) and number of collagen strings (#string) (9–72%) across all regions. In contrast, those initially staged as F3/4 at baseline showed a similar CPA and #string trend at 24 weeks. For regression patients, 27 parameters (25 in the CV region) in patients staged as F3/4 and 15 parameters (three in the PT and 12 in the PP regions) in those staged as F5/6 showed significant differences between the CHB and CHC groups at baseline. Following successful antiviral treatment, the pre- and post-treatment liver samples provided quantitative evidence of the heterogeneity of fibrotic features. qFibrosis has the potential to provide new insights into the characteristics of fibrosis regression in both patients with CHB and CHC as early as 24 weeks after antiviral therapy.

Resmetirom, a thyroid hormone beta (THR-β) agonist, has been shown to improve metabolic-dysfunction associated steatohepatitis (MASH) and fibrosis in patients with non-cirrhotic MASH in a Phase 3 serial liver biopsy study. The impact of resmetirom compared with placebo on histologic fibrosis features was evaluated using artificial intelligence (AI)-based digital pathology (qFibrosis) in the phase 3 MAESTRO-NASH trial. q-Fibrosis was a secondary analysis of liver biopsies from 966 patients with biopsy-confirmed MASH and fibrosis stages F1B, F2, or F3 enrolled in the multicentre, double-blind, placebo-controlled, phase 3 MAESTRO-NASH trial. Biopsies from baseline and Week 52 were assessed using second harmonic generation (SHG) and two-photon excitation fluorescence microscopy. Pre-specified assessment according to treatment was conducted of qFibrosis continuous values (qFC), categorical stages (qFS), and qSteatosis. Posthoc assessments were conducted of regional qFibrosis features and 30 clinical outcome-associated qFibrosis features including correlations of the 30 features with pathologist-assessed fibrosis improvement and non-invasive tests. Resmetirom 80 mg and 100 mg led to qFS improvement (decrease by ≥1) in 24.4% and 22.3% more patients than placebo, respectively, and reduced qFS worsening (increase by ≥1) relative to placebo, respectively (nominal, p≤0.001 for both doses). Mean placebo-corrected reductions in qFC were -0.95 (95% CI: -1.22, -0.69) and -1.08 (95% CI: -1.34, -0.81) for 80 mg and 100 mg, respectively. Six of the 30 clinical-outcome associated qFibrosis features from the portal tract and Zone 2 regions showed the highest positive correlations with pathologist-assessed fibrosis stage, and biomarkers including liver stiffness measures. Individual fibrosis features from the portal region and chicken wire fibrosis showed the strongest reduction by resmetirom. Resmetirom treatment led to significant improvements in qFC, qFS, and individual collagen features associated with fibrosis progression. These digital pathology findings support the antifibrotic efficacy of resmetirom and demonstrate the potential of AI-based quantification to help define the fibrogenic response in MASH. This study provides the first evidence that AI-based digital pathology, including qFibrosis and region-specific collagen features, sensitively detects antifibrotic treatment effects of resmetirom in MASH beyond the conventional ordinal staging based on a phase 3 pivotal trial. These continuous metrics highlight nuanced pathophysiological mechanisms of fibrosis progression and reversal in MASLD including early matrix remodeling and allowing for more granular, reproducible, and biologically plausible analysis. The ability to quantify spatially distinct collagen remodeling patterns, especially in portal tract and Zone 2 regions, could help refine the understanding of the anti-fibrotic effects of treatment for MASH including the impact to reduce fibrosis progression to cirrhosis and clinical outcomes. NCT03900429.

Fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD) is a prognostic indicator and clinical trial efficacy endpoint. Second-harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy images unstained tissue sections and, when integrated with artificial intelligence models, generates a continuous fibrosis value (qFibrosis) and ordinal qFibrosis stage. The impact of biopsy size and location on the accuracy of these approaches has not been assessed in MASLD, leaving quality assurance procedures undefined. One unstained section each from 100 hepatectomy/explant MASLD cases, 20 of each pathologist-assigned Non-alcoholic Steatohepatitis Clinical Research Network (NASH-CRN) fibrosis stage (F0–F4), were used to create virtual core biopsies by cropping regions from within the whole parent section. Regions varied in length (5–30 mm) with a fixed width of 0.9 mm, width (0.5–1.3 mm) with a fixed length of 15 mm, or position within the whole parent section. SHG/TPEF was used, and the qFibrosis continuous value and stage of the virtual core biopsies were determined for comparison with those of the whole parent section. The qFibrosis continuous value and stage correlated strongly with pathologist-assigned NASH-CRN stage (r_s = 0.92). Increasing the length and width of virtual biopsies increased the correlation between the qFibrosis continuous value and the agreement with the qFibrosis stage of the whole parent section, stabilising between 20–26 mm in length and 0.9 mm in width. The position within the tissue did not influence qFibrosis metrics. Longer (>20 mm) and wider (>0.9 mm) biopsies provide more accurate fibrosis assessment using SHG/TPEF. Biopsy position and orientation do not influence accuracy. Fibrosis assessment is an important prognostic indicator and clinical trial endpoint in metabolic dysfunction-associated steatotic liver disease, but liver biopsy sampling variation quality assurance has not been investigated for second-harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy quantification of fibrosis. Longer (>20 mm) and wider (>0.9 mm) biopsies allow for more accurate digital assessment of fibrosis. Clinical trials should incorporate suitable protocols to verify biopsy sizes that optimise digital fibrosis assessment using SHG/TPEF.

Fibrosis represents a highly conserved response to tissue injury. Assessing fibrosis is central in diagnostic pathology, evaluating treatment response and prognosis. Second harmonic generation digital pathology with artificial intelligence analyses provides unparalleled precision and granularity in quantifying tissue collagen in its natural, unstained environment.. This technology reveals new insights into the balance between fibrogenesis and fibrolysis, crucial in tracking disease evolution and treatment outcomes. This review describes applications of second harmonic generation digital pathology with artificial intelligence for detailed characterization of liver fibrosis, assessing treatment response in clinical trials, analyzing collagen features in other chronic diseases and cancers. Additionally, it offers a perspective on future developments in integrating various technologies into a comprehensive diagnostic workflow for more effective evaluation of therapy and disease prognosis.