Skip to main content

Advanced Search

Projected result for your current search:: 0

Search Operators:

You can use operators in your search query to narrow down your results even more. Click on the search operator to see an explanation of how it works.
Finds documents with exactly this word group, in exactly this word order and spelling (e.g. "employer branding").
Finds documents with both search terms (e.g. sales AND bonus).
Finds documents with one or the other search term (e.g. porsche OR volkswagen).
Finds documents with all search terms. Blank space is understood as AND (e.g. man robot production).
Finds documents with no appearance of the word behind NOT (e.g. ford NOT "harrison ford").
Finds documents where the search term is mentioned at least "n" times."n" may be any number (e.g. COUNT(gear)>8).
Finds documents with both search terms in any word order, permitting "n" words as a maximum distance between them. Best choose between 15 and 30 (e.g. NEAR(recruit, professionals, 20)).
Finds documents with the search term in word versions or composites. The asterisk * marks whether you wish them BEFORE, BEHIND, or BEFORE and BEHIND the search term (e.g. lightweight*, *lightweight, *lightweight*).
Finds documents with the search term in different spellings. "?" allows only one character (e.g. organi?ation).
Special characters are understood as AND (e.g. Miller Bros. & Sons).
 Log in
Top
Insights into Imaging

Inter-platform reproducibility of ultrasound-based fat fraction for evaluating hepatic steatosis in nonalcoholic fatty liver disease

Published in:
Authors
Sun Kyung Jeon
Jeong Min Lee
Published in
Insights into Imaging | Issue 1/2024

Abstract

Objectives

To evaluate the inter-platform reproducibility of ultrasound-based fat fraction examination in nonalcoholic fatty liver disease (NAFLD).

Methods

Patients suspected of having NAFLD were prospectively enrolled from January 2023. Ultrasound-based fat fraction examinations were performed using two different platforms (ultrasound-derived fat fraction [UDFF] and quantitative ultrasound-derived estimated fat fraction [USFF]) on the same day. The correlation between UDFF and USFF was assessed using Pearson correlation coefficient. Intraclass correlation coefficient (ICC), Bland–Altman analysis with 95% limits of agreement (LOAs), and the coefficient of variation (CV) were used to assess inter-platform reproducibility.

Results

A total of 41 patients (21 men and 20 women; mean age, 53.9 ± 12.6 years) were analyzed. Moderate correlation was observed between UDFF and USFF (Pearson’s r = 0.748; 95% confidence interval [CI]: 0.572–0.858). On Bland–Altman analysis, the mean difference between UDFF and USFF values was 1.3% with 95% LOAs ranging from -8.0 to 10.6%. The ICC between UDFF and USFF was 0.842 (95% CI: 0.703–0.916), with a CV of 29.9%.

Conclusion

Substantial inter-platform variability was observed among different ultrasound-based fat fraction examinations. Therefore, it is not appropriate to use ultrasound-based fat fraction values obtained from different vendors interchangeably.

Critical relevance statement

Considering the substantial inter-platform variability in ultrasound-based fat fraction assessments, caution is imperative when interpreting and comparing fat fraction values obtained from different ultrasound platforms in clinical practice.

Key points

• Inter-platform reproducibility of ultrasound-based fat fraction examinations is important for its clinical application.
• Significant variability across different ultrasound-based fat fraction examinations was observed.
• Using ultrasound-based fat fraction values from different vendors interchangeably is not advisable.

Graphical Abstract

Title
Inter-platform reproducibility of ultrasound-based fat fraction for evaluating hepatic steatosis in nonalcoholic fatty liver disease
Authors
Sun Kyung Jeon
Jeong Min Lee
Publication date
01-12-2024
Publisher
Springer Vienna
Published in
Insights into Imaging / Issue 1/2024
Electronic ISSN: 1869-4101
DOI
https://doi.org/10.1186/s13244-024-01611-0
This content is only visible if you are logged in and have the appropriate permissions.

Other articles of this Issue 1/2024

Prediction of lymph node metastasis in operable cervical cancer using clinical parameters and deep learning with MRI data: a multicentre study

Constructing and exploring neuroimaging projects: a survey from clinical practice to scientific research

The European MR safety landscape

  • Open Access
  • Statement

Fractal analysis of left ventricular trabeculae in heart failure with preserved ejection fraction patients with multivessel coronary artery disease

Correction: Standards for conducting and reporting consensus and recommendation documents: European Society of Cardiovascular Radiology policy from the Guidelines Committee

  • Open Access
  • Correction

Unlocking the potential: T1-weighed MRI as a powerful predictor of levodopa response in Parkinson’s disease

How can your team use biomarkers to improve management of AD? (Link opens in a new window)

0.5 AMA PRA Category 1 Credit(s)™

Our experts explore using biomarker tests and interpreting results, establishing a shared decision-making approach with patients and caregivers, and applying biomarker testing to guide treatment strategies.

This content is intended for healthcare professionals outside of the UK.

Supported by:
  • Lilly
Developed by: Springer Health+ IME
Learn more

How can you integrate PET into your practice? (Link opens in a new window)

1.5 AMA PRA Category 1 Credit(s)™

PET imaging is playing an increasingly critical role in managing AD. Our expert-led program will empower you with practical strategies and real-world case studies to effectively integrate it into clinical practice.

This content is intended for healthcare professionals outside of the UK.

Supported by:
  • Lilly
Developed by: Springer Health+ IME
Learn more
Image Credits
Alzheimer’s disease in the biomarker era: preparing the multidisciplinary care team/© Springer Healthcare IME, Navigating neuroimaging in Alzheimer’s care: Practical applications and strategies for integration/© Springer Health+ IME