Exploring changing attitudes to non-invasive liver fibrosis tests in secondary care pathways: comparison of two national surveys

Increasing engagement with liver disease management across the UK: a follow-up cross-sectional survey

BACKGROUND

Liver disease is an increasing cause of premature mortality. Early detection of liver disease in primary care gives opportunity to intervene and change outcomes. Engagement in liver disease care by NHS bodies responsible for primary care pathway development could drive improvements. The formation of integrated care systems (ICS) in England provides an opportunity to reassess engagement with liver disease nationally.

AIM

To update the level of engagement with community chronic liver disease management among ICSs and health authorities across the UK.

DESIGN & SETTING

A cross-sectional follow-up survey to ICS and UK health boards.

METHOD

Questions used for a previous survey in 2020 were adapted and sent electronically to NHS bodies responsible for health care across the UK, using a freedom of information request. Quantitative analysis was undertaken using Microsoft Excel.

RESULTS

There were 67 responses from 68 possible ICS and health board areas, representing 99% UK coverage. Twenty-seven per cent had a named individual responsible for liver disease. Monitoring of local liver disease health statistics happened in 34% of all UK areas. Comprehensive care pathways were available in n = 24/67 (36%) of areas, an increase from 26% in the 2020 survey. Areas with no liver pathways in place fell from 58% to 36% between the two surveys. Regional variations persist, with Wales and Scotland moving towards comprehensive coverage. Almost double the number of areas were making use of transient elastography within community pathways of care, up from 25% to 46%.

CONCLUSION

The results of this re-survey highlight improvements, but emphasise the need to build on regional success to further reduce inequality in care commissioning.

Improving HCC Prognostic Models after Liver Resection by AI-Extracted Tissue Fiber Framework Analytics

Despite advances in diagnostic and treatment technologies, predicting outcomes of patients with hepatocellular carcinoma (HCC) remains a challenge. Prognostic models are further obscured by the variable impact of the tumor properties and the remaining liver parenchyma, often affected by cirrhosis or non-alcoholic fatty liver disease that tend to precede HCC. This study investigated the prognostic value of reticulin and collagen microarchitecture in liver resection samples. We analyzed 105 scanned tissue sections that were stained using a Gordon and Sweet's silver impregnation protocol combined with Picric Acid-Sirius Red. A convolutional neural network was utilized to segment the red-staining collagen and black linear reticulin strands, generating a detailed map of the fiber structure within the HCC and adjacent liver tissue. Subsequent hexagonal grid subsampling coupled with automated epithelial edge detection and computational fiber morphometry provided the foundation for region-specific tissue analysis. Two penalized Cox regression models using LASSO achieved a concordance index (C-index) greater than 0.7. These models incorporated variables such as patient age, tumor multifocality, and fiber-derived features from the epithelial edge in both the tumor and liver compartments. The prognostic value at the tumor edge was derived from the reticulin structure, while collagen characteristics were significant at the epithelial edge of peritumoral liver. The prognostic performance of these models was superior to models solely reliant on conventional clinicopathologic parameters, highlighting the utility of AI-extracted microarchitectural features for the management of HCC.