Aramchol improves hepatic fibrosis in metabolic dysfunction-associated steatohepatitis: Results of multimodality assessment using both conventional and digital pathology.

BACKGROUND AND AIMS: Antifibrotic trials rely on conventional pathology despite recognized limitations. We compared single-fiber digital image analysis with conventional pathology to quantify the antifibrotic effect of Aramchol, a stearoyl-CoA desaturase 1 inhibitor in development for metabolic dysfunction-associated steatohepatitis. APPROACH AND RESULTS: Fifty-one patients with metabolic dysfunction-associated steatohepatitis enrolled in the open-label part of the ARMOR trial received Aramchol 300 mg BID and had paired pre-post treatment liver biopsies scored by consensus among 3 hepatopathologists, and separately assessed by a digital image analysis platform (PharmaNest) that generates a continuous phenotypic Fibrosis Composite Severity (Ph-FCS) score. Fibrosis improvement was defined as: ≥1 NASH Clinical Research Network (NASH-CRN) stage reduction; "improved" by ranked pair assessment; reduction in Ph-FCS ("any" for ≥0.3 absolute reduction and "substantial" for ≥25% relative reduction). Fibrosis improved in 31% of patients (NASH-CRN), 51% (ranked pair assessment), 74.5% (any Ph-FCS reduction), and 41% (substantial Ph-FCS reduction). Most patients with stable fibrosis by NASH-CRN or ranked pair assessment had a Ph-FCS reduction (a third with substantial reduction). Fibrosis improvement increased with treatment duration: 25% for <48 weeks versus 39% for ≥48 weeks by NASH-CRN; 43% versus 61% by ranked pair assessment, mean Ph-FCS reduction -0.54 (SD: 1.22) versus -1.72 (SD: 1.02); Ph-FCS reduction (any in 54% vs. 100%, substantial in 21% vs. 65%). The antifibrotic effect of Aramchol was corroborated by reductions in liver stiffness, Pro-C3, and enhanced liver fibrosis. Changes in Ph-FCS were positively correlated with changes in liver stiffness. CONCLUSIONS: Continuous fibrosis scores generated in antifibrotic trials by digital image analysis quantify antifibrotic effects with greater sensitivity and a larger dynamic range than conventional pathology.

Incidental findings during donor liver assessment: Single center experience.

Intraoperative consultation of donor liver is an important part of transplant evaluation and determination of liver eligibility. In this study, we describe incidental pathologic findings discovered during the pretransplant evaluation of liver donors in our Institution from 1/2010 to 12/2022. During this 13-year period 369 intraoperative consultations from 262 liver donors were performed. Of those cases, incidental findings were identified in 22 cases (5.9 %) from 19 donors (7.3 %); two donors had more than one lesion. The median age of this subset of patients was 53 years (range: 18-70) and females predominated (63 %). Sixteen of the donors had abnormal findings in the liver: 6 bile duct hamartoma (BDH), 5 hyalinized nodule with Histoplasma capsulatum, 5 focal nodular hyperplasia (FNH), 2 bile duct adenomas (BDA), 1 biliary cyst and 1 hemangioma. One donor had both FNH and a BDH. One BDH and 1 BDA case was misdiagnosed as malignancy during the frozen section evaluation. Three donors had extrahepatic pathologies: a pancreatic tail schwannoma, a low-grade appendiceal mucinous neoplasm, and a lymph node with metastatic endometrial endometrioid adenocarcinoma. Of the 19 livers, the final organ disposition was available for 9: 6 were transplanted (67 %) and 3 were discarded (33 %). Two of the 3 discarded organs were misdiagnosed BDH and BDA cases, and one was incorrectly reported as having 90 % microvesicular steatosis during the frozen assessment. We present the clinicopathologic characteristics of liver donors with incidental findings during the pre-transplant evaluation which could lead to unwarranted graft dismissal if misdiagnosed. Additionally, incidental fungal infections can have implications for immunosuppressive therapy and the decision to use or reject the graft.

Automated assessment of steatosis in murine fatty liver.

Although mice are commonly used to study different aspects of fatty liver disease, currently there are no validated fully automated methods to assess steatosis in mice. Accurate detection of macro- and microsteatosis in murine models of fatty liver disease is important in studying disease pathogenesis and detecting potential hepatotoxic signature during drug development. Further, precise quantification of macrosteatosis is essential for quantifying effects of therapies. Here, we develop and validate the performance of automated classifiers built using image processing and machine learning methods for detection of macro- and microsteatosis in murine fatty liver disease and study the correlation of automated quantification of macrosteatosis with expert pathologist's semi-quantitative grades. The analysis is performed on digital images of 27 Hematoxylin & Eosin stained murine liver biopsy samples. An expert liver pathologist scored the amount of macrosteatosis and also annotated macro- and microsteatosis lesions on the biopsy images using a web-application. Using these annotations, supervised machine learning and image processing techniques, we created classifiers to detect macro- and microsteatosis. For macrosteatosis prediction, the model's precision, sensitivity and area under the receiver operator characteristic (AUROC) were 94.2%, 95%, 99.1% respectively. When correlated with pathologist's semi-quantitative grade of steatosis, the model fits with a coefficient of determination value of 0.905. For microsteatosis prediction, the model has precision, sensitivity and AUROC of 79.2%, 77%, 78.1% respectively. Validation by the expert pathologist of classifier's predictions made on unseen images of biopsy samples showed 100% and 63% accuracy for macro- and microsteatosis, respectively. This novel work demonstrates that fully automated assessment of steatosis is feasible in murine liver biopsies images. Our classifier has excellent sensitivity and accuracy for detection of macrosteatosis in murine fatty liver disease.