P59 Seeing through the haze: incorporating LI-RADS in HCC surveillance ultrasound reports

<h3>Background</h3> Liver cancer is the sixth most common cancer worldwide. Hepatocellular carcinoma (HCC) is the most prevalent primary hepatological malignancy. Poor historical survival rates have improved, partly due to earlier detection of liver lesions by surveillance of those at risk of developing HCC. The European Association for the Study of the Liver (EASL) and American Association for the Study of Liver Diseases (AASLD) both recommend biannual surveillance in high risk populations using ultrasound (US) examination. Whilst the overall sensitivity of US for any stage HCC is 84%, this sensitivity drops when the examination is hampered by poor visualisation, related to bowel gas, or body habitus for example. The US Liver Imaging Reporting and Data System (LI-RADS) is a standardised system for technique, interpretation and reporting of US examinations in those undergoing HCC surveillance. At the current time, it has not been widely incorporated into US reporting across Northern Ireland. <h3>Objectives</h3> To identify the frequency of HCC surveillance US where the report suggests there are limitations due to poor visualization. Furthermore, we aimed to categorise these reports according to LI-RADS visualization score. <h3>Method</h3> All patients undergoing HCC surveillance US during September 2024 were identified and their US reports reviewed. US examinations limited by poor visualisation were defined as any report containing comments regarding limitations due to body habitus, overlying bowel gas, or other comment suggesting limited visualisation. Once identified, these examinations were reviewed by one of two consultant radiologists and categorised according to the LI-RADS visualization score. <h3>Results</h3> During September 2024, 194 patients were scheduled to have HCC surveillance US. Alternative imaging (CT/MRI) was carried out in 20 patients, 3 did not attend and 171 underwent ultrasound. Of these 171, 40 scan reports (23.4%) were categorised as being limited by poor visualisation, 18 due to bowel gas, 12 body habitus and the remainder due to other miscellaneous reasons. Consultant radiologist review categorised these scans as LIRADS A – 10, B – 19 and C- 11. Review of the next scheduled HCC surveillance US in these 40 patients demonstrated limitations due to poor visualisation once again in 19, with 20 considered satisfactory. One patient failed to attend. <h3>Conclusion</h3> This review highlights the challenges in HCC surveillance associated with poor visualisation. Implementing LI-RADS categorisation into US reports will allow standardisation and tailoring of follow up imaging, potentially enabling earlier HCC detection, a reduction in unnecessary testing and its associated costs.

Authors

Institutions

Publication Details

Published
2025-10-01
DOI
https://doi.org/10.1136/gutjnl-2025-basl.75
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

P59 Seeing through the haze: incorporating LI-RADS in HCC surveillance ultrasound reports

Hannah McDowell, Ian Cadden, Jonathan Reaney, Anton Collins et al.
Radiomics and Machine Learning in Medical Imaging
article

P59 Seeing through the haze: incorporating LI-RADS in HCC surveillance ultrasound reports

Hannah McDowell, Ian Cadden, Jonathan Reaney, Anton Collins, Raghu Sathyanarayana, Grainne McNally, Hannah Healy
article en

Abstract

<h3>Background</h3> Liver cancer is the sixth most common cancer worldwide. Hepatocellular carcinoma (HCC) is the most prevalent primary hepatological malignancy. Poor historical survival rates have improved, partly due to earlier detection of liver lesions by surveillance of those at risk of developing HCC. The European Association for the Study of the Liver (EASL) and American Association for the Study of Liver Diseases (AASLD) both recommend biannual surveillance in high risk populations using ultrasound (US) examination. Whilst the overall sensitivity of US for any stage HCC is 84%, this sensitivity drops when the examination is hampered by poor visualisation, related to bowel gas, or body habitus for example. The US Liver Imaging Reporting and Data System (LI-RADS) is a standardised system for technique, interpretation and reporting of US examinations in those undergoing HCC surveillance. At the current time, it has not been widely incorporated into US reporting across Northern Ireland. <h3>Objectives</h3> To identify the frequency of HCC surveillance US where the report suggests there are limitations due to poor visualization. Furthermore, we aimed to categorise these reports according to LI-RADS visualization score. <h3>Method</h3> All patients undergoing HCC surveillance US during September 2024 were identified and their US reports reviewed. US examinations limited by poor visualisation were defined as any report containing comments regarding limitations due to body habitus, overlying bowel gas, or other comment suggesting limited visualisation. Once identified, these examinations were reviewed by one of two consultant radiologists and categorised according to the LI-RADS visualization score. <h3>Results</h3> During September 2024, 194 patients were scheduled to have HCC surveillance US. Alternative imaging (CT/MRI) was carried out in 20 patients, 3 did not attend and 171 underwent ultrasound. Of these 171, 40 scan reports (23.4%) were categorised as being limited by poor visualisation, 18 due to bowel gas, 12 body habitus and the remainder due to other miscellaneous reasons. Consultant radiologist review categorised these scans as LIRADS A – 10, B – 19 and C- 11. Review of the next scheduled HCC surveillance US in these 40 patients demonstrated limitations due to poor visualisation once again in 19, with 20 considered satisfactory. One patient failed to attend. <h3>Conclusion</h3> This review highlights the challenges in HCC surveillance associated with poor visualisation. Implementing LI-RADS categorisation into US reports will allow standardisation and tailoring of follow up imaging, potentially enabling earlier HCC detection, a reduction in unnecessary testing and its associated costs.

University of Ulster (GB), Royal Victoria Hospital (GB)
Openalex Percentile: Top 58%
Radiomics and Machine Learning in Medical Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.