Non-invasive ultrasound assessment of chronic liver disease: current position and future directions for a “one-stop” liver ultrasound approach
Year: 2026
Type: article
Abstract: The integration of the multitude of ultrasound techniques into a "one-stop" liver clinic model will revolutionize the management of liver diseases. This approach streamlines patient care by providing immediate imaging assessment, facilitating prompt diagnosis, and expediting treatment plans. The traditional ultrasound methods of B-mode imaging and Doppler techniques have been supplemented by the newer techniques of tissue elastography, fat quantification, and contrast-enhanced ultrasound-termed multiparametric ultrasound. The deployment of these techniques to establish in more detail the underlying status of liver disease has been profound. The encompassing ultrasound techniques have allowed the ultrasound practitioner to establish a comprehensive assessment of liver disease, allowing further accurate management, and negating the need for additional, often more expensive, imaging to establish the diagnosis. This paper explores the implementation, benefits, and challenges of ultrasound-based one-stop liver clinics, emphasizing their impact on patient outcomes and healthcare efficiency. A detailed assessment of the techniques and their position in the diagnostic armamentarium is reviewed with a comprehensive overview established. CRITICAL RELEVANCE STATEMENT: Multiparametric liver ultrasound integrating B-mode, Doppler, CEUS, elastography and fat quantification provides a practical, low-cost one-stop pathway for staging chronic liver disease, assessing portal hypertension surrogates and characterizing incidental lesions, thereby speeding up treatment. KEY POINTS: Ultrasound is the first-line imaging investigation for liver disease, with established criteria on B-mode imaging for steatosis and cirrhosis. Multiparametric ultrasound integrates morphology, hemodynamics, fibrosis, steatosis, and lesion assessment. A one-stop liver ultrasound clinic accelerates decisions and reduces additional imaging.
Source: Insights into Imaging
Institutions: King's College London, King's College Hospital NHS Foundation Trust, King's College Hospital, Dokuz Eylül University, Ludwig-Maximilians-Universität München, Imperial College Healthcare NHS Trust, Imperial College London, Gdańsk Medical University, Sapienza University of Rome, Humboldt-Universität zu Berlin, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Charité - Universitätsmedizin Berlin, Russian Medical Academy of Continuous Professional Education, Copenhagen University Hospital, Rigshospitalet
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,290
OpenAlex ID: W7156247287
