Evidence map›Paper›PMID 38781055›Full record

ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2024

Sticky Hard-Sphere Model for Characterizing Tumor Microstructure via Quantitative Ultrasound.

Quang Tran, William D O'Brien, Aiguo Han

Abstract read
In one paragraph

Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

3 authors.

Quang Tran
William D O'Brien
Aiguo Han

Funding

QUS Technology for Identifying At-Risk Women for Spontaneous Preterm BirthR01HD089935 · NICHD · UNIVERSITY OF ILLINOIS AT CHICAGO · PI MCFARLIN, BARBARA L, O'BRIEN, WILLIAM D. · 2017 to 2021
$3.1M
Tumor Detection and Classification using QUS Technology's Structure FunctionR01CA226528 · NCI · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI O'BRIEN, WILLIAM D. · 2019 to 2024
$2.3M
NCI NIH HHS R01 CA226528NICHD NIH HHS R01 HD089935
6 · The paper itself

Abstract

The use of the structure function (SF) to model interscatterer contribution to ultrasonic scattering is a major step to improve the capability and accuracy of quantitative ultrasound (QUS) and tissue characterization. However, existing QUS-based SF models rely on the hard-sphere (HS) model, which is limited in its applicability for complex scatterer distributions in real tissue. This article introduces the sticky HS (SHS) model for QUS and tissue characterization, which considers a very short-range attractive potential that accounts for the adhesive nature of biological cells and yields a new parameter called stickiness. Herein, the analytical SF expression is presented for monodisperse scatterer size and validated using simulations of scatterer distributions with varying degrees of grouping and volume fractions (0.16, 0.32, and 0.40) over the frequency range from 15 to 110 MHz. The SHS model is applied to three mammary tumor types with differing spatial distributions of tumor cells. The histology-derived SF is computed by considering the nuclei as the main sources of scattering. The results show that the SHS model provides more accurate scatterer radius and volume fraction estimates than the HS model when fit to histology-derived SF versus frequency curves. Furthermore, the new stickiness parameter provided by SHS is sensitive to the grouping structure in tumor cell distribution. This stickiness parameter, combined with the radius and volume fraction estimated from the SHS model, enables better differentiation between different tumor types than using the radius and volume fraction obtained from the HS model. This study demonstrates the potential of the SHS model to improve the QUS tissue characterization.

Indexed as

UltrasonographyAnimalsComputer SimulationFemaleHumansImage Processing, Computer-AssistedMiceModels, Biological

Identifiers

PMID38781055
PMCPMC11426106

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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.