Evidence map›Paper›PMID 36681609›Full record

ArticleUltrasound in medicine & biology2023

High-Resolution Ultrasound Characterization of Local Scattering in Cancer Tissue.

Mawia Khairalseed, Kenneth Hoyt

Open access · greenAbstract read
In one paragraph

Article in Ultrasound in medicine & biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
4.6field-weighted citation impact, top 5% 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

8 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. 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

2 authors at 1 institution in 1 country.

Mawia KhairalseedDepartment of Bioengineering, University of Texas at Dallas, Richardson, Texas, USA.
Kenneth HoytDepartment of Bioengineering, University of Texas at Dallas, Richardson, Texas, USA. Electronic address: kenneth.hoyt@utdallas.edu.
The University of Texas at Dallas · US

Funding

Multifrequency ultrasound imaging for improved breast tissue characterizationR01CA269973 · NCI · UNIVERSITY OF TEXAS DALLAS · PI HOYT, KENNETH · 2022 to 2025
$2.8M
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
NCI NIH HHS R01 CA269973NIBIB NIH HHS R01 EB025841NIDDK NIH HHS R01 DK126833
6 · The paper itself

Abstract

Ultrasound (US) has afforded an approach to tissue characterization for more than a decade. The challenge is to reveal hidden patterns in the US data that describe tissue function and pathology that cannot be seen in conventional US images. Our group has developed a high-resolution analysis technique for tissue characterization termed H-scan US, an imaging method used to interpret the relative size of acoustic scatterers. In the present study, the objective was to compare local H-scan US image intensity with registered histologic measurements made directly at the cellular level. Human breast cancer cells (MDA-MB 231, American Type Culture Collection, Manassas, VA, USA) were orthotopically implanted into female mice (N = 5). Tumors were allowed to grow for approximately 4 wk before the study started. In vivo imaging of tumor tissue was performed using a US system (Vantage 256, Verasonics Inc., Kirkland, WA, USA) equipped with a broadband capacitive micromachined ultrasonic linear array transducer (Kolo Medical, San Jose, CA, USA). A 15-MHz center frequency was used for plane wave imaging with five angles for spatial compounding. H-scan US image reconstruction involved use of parallel convolution filters to measure the relative strength of backscattered US signals. Color codes were applied to filter outputs to form the final H-scan US image display. For histologic processing, US imaging cross-sections were carefully marked on the tumor surface, and tumors were excised and sliced along the same plane. By use of optical microscopy, whole tumor tissue sections were scanned and digitized after nuclear staining. US images were interpolated to have the same number of pixels as the histology images and then spatially aligned. Each nucleus from the histologic sections was automatically segmented using custom MATLAB software (The MathWorks Inc., Natick, MA, USA). Nuclear size and spacing from the histology images were then compared with local H-scan US image features. Overall, local H-scan US image intensity exhibited a significant correlation with both cancer cell nuclear size (R

Indexed as

NeoplasmsAcousticsAnimalsFemaleHumansImage Processing, Computer-AssistedMiceUltrasonographyAcoustic scatterersCancerHistologic analysisH-Scan ultrasoundTissue characterizationUltrasound imaging

Identifiers

PMID36681609
PMCPMC9974749
OpenAlexW4317494006

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.