Quantitative ultrasound (QUS) in the evaluation of liver steatosis : data reliability in different respiratory phases and body positions

© 2024. The Author(s)..

Liver steatosis is the most common chronic liver disease and affects 10-24% of the general population. As the grade of disease can range from fat infiltration to steatohepatitis and cirrhosis, an early diagnosis is needed to set the most appropriate therapy. Innovative noninvasive radiological techniques have been developed through MRI and US. MRI-PDFF is the reference standard, but it is not so widely diffused due to its cost. For this reason, ultrasound tools have been validated to study liver parenchyma. The qualitative assessment of the brightness of liver parenchyma has now been supported by quantitative values of attenuation and scattering to make the analysis objective and reproducible. We aim to demonstrate the reliability of quantitative ultrasound in assessing liver fat and to confirm the inter-operator reliability in different respiratory phases. We enrolled 45 patients examined during normal breathing at rest, peak inspiration, peak expiration, and semi-sitting position. The highest inter-operator agreement in both attenuation and scattering parameters was achieved at peak inspiration and peak expiration, followed by semi-sitting position. In conclusion, this technology also allows to monitor uncompliant patients, as it grants high reliability and reproducibility in different body position and respiratory phases.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:129

Enthalten in:

La Radiologia medica - 129(2024), 4 vom: 09. Apr., Seite 549-557

Sprache:

Englisch

Beteiligte Personen:

Rocca, Aldo [VerfasserIn]
Komici, Klara [VerfasserIn]
Brunese, Maria Chiara [VerfasserIn]
Pacella, Giulia [VerfasserIn]
Avella, Pasquale [VerfasserIn]
Di Benedetto, Chiara [VerfasserIn]
Caiazzo, Corrado [VerfasserIn]
Zappia, Marcello [VerfasserIn]
Brunese, Luca [VerfasserIn]
Vallone, Gianfranco [VerfasserIn]

Links:

Volltext

Themen:

Artificial intelligence
Formal Methods
Journal Article
Pancreatitis
Radiomics

Anmerkungen:

Date Completed 17.04.2024

Date Revised 25.04.2024

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1007/s11547-024-01786-y

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM370021878