Evidence map›Paper›PMID 31545719›Full record

ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2020

Modeling Uncertainty of Strain Ratio Measurements in Ultrasound Breast Strain Elastography: A Factorial Experiment.

David Rosen, Jingfeng Jiang

Abstract read
In one paragraph

Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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.

David Rosen
Jingfeng Jiang

Funding

Virtual Breast Project: Improving Noninvasive Characterization of TumorsR15CA179409 · NCI · MICHIGAN TECHNOLOGICAL UNIVERSITY · PI JIANG, JINGFENG · 2014 to 2014
$453k
Elastography-based Analytics for Benign and Malignant Breast DiseaseR15EB026197 · NIBIB · MICHIGAN TECHNOLOGICAL UNIVERSITY · PI JIANG, JINGFENG · 2018 to 2018
$450k
NCI NIH HHS R15 CA179409NIBIB NIH HHS R15 EB026197
6 · The paper itself

Abstract

Strain elastography (SE) is a technique in which images of localized tissue strains are used to detect the relative stiffness of tissues. The application of SE in differentiating malignant breast lesions from benign ones has been broadly investigated. The strain ratio (SR) between the background and the breast tumor has been used and its results have been mixed. Due to the complex nature of tissue elasticity and how it relates to the strain fields measured in SE, the exact reason is not known. In this study, we apply a novel design-of-experiments-based metamodeling approach to mechanical simulation of SE in the human breast. To our knowledge, such a study has not been reported in the ultrasound SE literature. More specifically, we first conduct a screening study to identify the biomechanical factors/simulation inputs that most strongly determine SR. We then apply a response surface experimental design to these factors to produce a metamodel of SR as a function of said factors. Results from the screening study suggest that the SR measurements are primarily influenced by three factors: the initial shear modulus of the lesion, the elastic nonlinearity of the lesion, and the precompression applied during acquisition. In order to investigate the implications of these results, stochastic inputs for these three factors associated with the malignant and benign cases were applied to the resulting response surface. The resulting optimal cutoffs, sensitivity, and specificity were generally in line with a majority (>60%) of 19 clinical trials in the literature.

Indexed as

BreastBreast NeoplasmsComputer SimulationElasticity Imaging TechniquesElastic ModulusFemaleHumansSensitivity and SpecificityUltrasonography, Mammary

Identifiers

PMID31545719
PMCPMC8011866

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Registered trials

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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.