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Improving accuracy of liver health assessment for better decision support in cancer

Liver health assessment and visualization on a single platform.

 

  • Patient-friendly reports


  • Seamless integration into workflow through a PACS-integrated cloud-based service


  • AI-driven delineation of liver volume and individual Couinaud segments


  • MRI-based, therefore no radiation risk, giving your patients a safer experience

Key Benefits

Stratify Risk

Identification of patients at risk of poor post-operative outcomes to inform surgical decision-making.

Improve Outcomes

Clinically validated metrics to improve the accuracy of pre-operative liver health assessment1-5.

Inform Clinical Decisions

Quantitative assessment of liver health and volume presented in one comprehensive report.

What is Hepatica?

Hepatica is a clinical and surgical decision support tool based on non-invasive quantitative multiparametric MRI. It enables more informed preoperative decision making and better risk-stratification, therefore improving post-operative outcomes. It does this by providing simultaneous evaluation of liver health (fibroinflammation and fat) and volumetry with AI-driven delineation of the liver and individual Couinaud segments in a single report of quantitative metrics. Hepatica has demonstrated early utility in identifying patients at risk of poor outcomes6, with the potential to inform surgical planning and realize significant cost savings through lowering the post-surgical complication rate and associated in-patient hospital stay.

Couinaud segment analysis

Liver fibroinflammation, fat and volume quantified across Couinaud segments to inform surgical decision-making

Whole liver analysis

Built on LiverMultiScan technology that provides validated biomarkers of liver health and predicts clinical outcomes1-4

Interested in finding out more?

Get this for your patients Check out our Hepatica research

1. Banerjee, R., et al. (2014). J Hepatol, 60(1), 69–77.
2. Pavlides, M., et al. (2017). Liver Int,, 37(7), 1065–1073.
3. Pavlides, M., et al. (2016). J Hepatol, 64(2), 308–315.
4. Jayaswal, N.A., et al. (2020). Liver Int. Online ahead of publication.
5. Bachtiar, V., et al. (2019). PLOS ONE, 14(4), e0214921.
6. Mole et al. (2020). PLOS ONE, Accepted.