The latest medical research on Liver Cancer

Steatotic liver disease (SLD) is generally considered to represent a hepatic manifestation of metabolic syndrome and includes a disease spectrum comprising isolated steatosis, metabolic dysfunction-associated steatohepatitis, liver fibrosis and ultimately cirrhosis. A better understanding of the detailed underlying pathogenic mechanisms of this transition is crucial for the design of new and efficient therapeutic interventions. Thymocyte differentiation antigen (Thy-1, also known as CD90) expression on fibroblasts controls central functions relevant to fibrogenesis, including proliferation, apoptosis, cytokine responsiveness, and myofibroblast differentiation.

The impact of Thy-1 on the development of SLD and progression to fibrosis was investigated in high-fat diet (HFD)-induced SLD wild-type and Thy-1-deficient mice. In addition, the serum soluble Thy-1 (sThy-1) concentration was analysed in patients with metabolic dysfunction-associated SLD stratified according to steatosis, inflammation, or liver fibrosis using noninvasive markers.

We demonstrated that Thy-1 attenuates the development of fatty liver and the expression of profibrogenic genes in the livers of HFD-induced SLD mice. Mechanistically, Thy-1 directly inhibits the profibrotic activation of nonparenchymal liver cells. In addition, Thy-1 prevents palmitic acid-mediated amplification of the inflammatory response of myeloid cells, which might indirectly contribute to the pronounced development of liver fibrosis in Thy-1-deficient mice. Serum analysis of patients with metabolically associated steatotic liver disease syndrome revealed that sThy-1 expression is correlated with liver fibrosis status, as assessed by liver stiffness, the Fib4 score, and the NAFLD fibrosis score.

Our data strongly suggest that Thy-1 may function as a fibrosis-protective factor in mouse and human SLD.

Primary biliary cholangitis (PBC) is a chronic, immune-mediated liver disease that can lead to fibrosis and cirrhosis. In this cohort study, we aimed to investigate morbidity and mortality in conjunction with metabolomic changes of PBC in a UK population-based cohort.

454 participants with PBC and 908 propensity score (age, sex, BMI, ethnicity) matched controls without liver disease were included in the study. A subset of participants with PBC and controls were analysed for their metabolomic profile. Further, PBC-associated comorbidities were investigated by PheWAS analysis. Lastly, we assessed causes of death in individuals with PBC using a Fine and Grey competing-risks regression model.

Compared to the control group, various pathways associated with the metabolism of amino acids, lipids, and liver biochemistry were significantly enriched in individuals with PBC. We found reduced levels of S-HDL-cholesterol and Glycoprotein Acetyls in individuals with PBC as well as an association with diseases of the circulatory system. Notably, PBC individuals had a higher prevalence of digestive diseases, autoimmune diseases, cardiovascular diseases, anaemias, mental disorders, and urinary tract infections compared to the control group. Strikingly, the overall mortality was almost three times higher in the PBC group compared to the control group, with diseases of the digestive system accounting for a significant elevation of the death rate. A subsequent analysis, enhanced by propensity score matching that included the APRI score, demonstrated that the observed morbidity could not be exclusively attributed to advanced hepatic disease.

Our study provides a detailed perspective on the morbidity of individuals with PBC. The exploration of potential effects of disease state on morbidity suggest that early detection and early treatment of PBC could enhance patient prognosis and prevent the onset of comorbid diseases. Finally, the metabolomic alterations could represent a link between the pathophysiological processes underlying PBC development, progression, and associated morbidity.

The Liver Imaging Reporting and Data System (LI-RADS) offers a standardized approach for imaging hepatocellular carcinoma. However, the diverse styles and structures of radiology reports complicate automatic data extraction. Large language models hold the potential for structured data extraction from free-text reports. Our objective was to evaluate the performance of Generative Pre-trained Transformer (GPT)-4 in extracting LI-RADS features and categories from free-text liver magnetic resonance imaging (MRI) reports.

Three radiologists generated 160 fictitious free-text liver MRI reports written in Korean and English, simulating real-world practice. Of these, 20 were used for prompt engineering, and 140 formed the internal test cohort. Seventy-two genuine reports, authored by 17 radiologists were collected and de-identified for the external test cohort. LI-RADS features were extracted using GPT-4, with a Python script calculating categories. Accuracies in each test cohort were compared.

On the external test, the accuracy for the extraction of major LI-RADS features, which encompass size, nonrim arterial phase hyperenhancement, nonperipheral 'washout', enhancing 'capsule' and threshold growth, ranged from .92 to .99. For the rest of the LI-RADS features, the accuracy ranged from .86 to .97. For the LI-RADS category, the model showed an accuracy of .85 (95% CI: .76, .93).

GPT-4 shows promise in extracting LI-RADS features, yet further refinement of its prompting strategy and advancements in its neural network architecture are crucial for reliable use in processing complex real-world MRI reports.