Evidence map›Paper›PMID 34315619›Full record

ArticleUltrasound in medicine & biology2021

Clusters of Ultrasound Scattering Parameters for the Classification of Steatotic and Normal Livers.

Jihye Baek, Sedigheh S Poul, Lokesh Basavarajappa, Shreya Reddy, Haowei Tai, Kenneth Hoyt, Kevin J Parker

Open access · greenAbstract read
In one paragraph

Article in Ultrasound in medicine & biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
5.0field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 44 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Jihye BaekDepartment of Electrical and Computer Engineering, University of Rochester, Rochester, New York, USA.
Sedigheh S PoulDepartment of Mechanical Engineering, University of Rochester, Rochester, New York, USA.
Lokesh BasavarajappaDepartment of Bioengineering, University of Texas at Dallas, Richardson, Texas, USA.
Shreya ReddyDepartment of Bioengineering, University of Texas at Dallas, Richardson, Texas, USA.
Haowei TaiDepartment of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, Texas, USA.
Kenneth HoytDepartment of Bioengineering, University of Texas at Dallas, Richardson, Texas, USA; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Kevin J ParkerDepartment of Electrical and Computer Engineering, University of Rochester, Rochester, New York, USA. Electronic address: kevin.parker@rochester.edu.
The University of Texas at Dallas · USUniversity of Rochester · US

Funding

Multiparametric ultrasound imaging for early detection of nonalcoholic fatty liver diseaseR01DK126833 · NIDDK · UNIVERSITY OF TEXAS DALLAS · PI HOYT, KENNETH · 2021 to 2024
$1.6M
3D Super-Resolution Ultrasound Imaging for Cancer Detection and Treatment MonitoringR01EB025841 · NIBIB · UNIVERSITY OF TEXAS DALLAS · PI HOYT, KENNETH · 2019 to 2022
$1.3M
The H-scan approach to classifying ultrasound echoesR21EB025290 · NIBIB · UNIVERSITY OF ROCHESTER · PI PARKER, KEVIN J · 2018 to 2019
$420k
NIBIB NIH HHS R01 EB025841NIBIB NIH HHS R21 EB025290NIDDK NIH HHS R01 DK126833
6 · The paper itself

Abstract

The study of ultrasound tissue interactions in fatty livers has a long history with strong clinical potential for assessing steatosis. Recently we proposed alternative measures of first- and second-order statistics of echoes from soft tissues, namely, the H-scan, which is based on a matched filter approach, to quantify scattering transfer functions and the Burr distribution to model speckle patterns. Taken together, these approaches produce a multiparameter set that is directly related to the fundamentals of ultrasound propagation in tissue. To apply this approach to the problem of assessing steatotic livers, these analyses were applied to in vivo rat livers (N=21) under normal feeding conditions or after receiving a methionine- and choline-deficient diet that produces steatosis within a few weeks. Ultrasound data were acquired at baseline and again at weeks 2 and 6 before applying the H-scan and Burr analyses. Furthermore, a classification technique known as the support vector machine was then used to find clusters of the five parameters that are characteristic of the different steatotic liver conditions as confirmed by histologic processing of excised liver tissue samples. With the in vivo multiparametric ultrasound measurement approach and determination of clusters, steatotic can be discriminated from normal livers with 100% accuracy in a rat animal model.

Indexed as

Fatty LiverLiverAnimalsDisease Models, AnimalMethionineRatsUltrasonographyMethionineH-scan ultrasoundPrincipal component analysisSpeckleSteatosisSupport vector machineTissue characterizationUltrasound scatter

Identifiers

PMID34315619
PMCPMC8445071
OpenAlexW3184871666

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.