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Cited by 21Elasticity and Material ModelingUltrasound Imaging and ElastographyCellular Mechanics and Interactions

Quantitative diagnosis of breast tumors by characterization of viscoelastic behavior of healthy breast tissue

Madani, N., Mojra, A.·2017·Journal of the Mechanical Behavior of Biomedical Materials
DOI 10.1016/j.jmbbm.2017.01.044
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Abstract

The main objective of the present study is to propose a model for characterizing the viscoelastic behavior of the healthy breast tissue. The study population consisted of 60 healthy regions of the breast tissue belonged to 9 female cases. To accomplish this, the attending cases were examined by a robot-assisted device and the mechanical stress resulted from an applied compressive strain was measured. Correlation between the experimental stress and the strain data identifies the breast tissue mechanical behavior. The tissue behavior was modeled by a five-element Maxwell-Wiechert model called model E. The model was personalized for every attending case via its coefficients based on a personalized diagnosis idea. The model performance was assessed by measuring the Mean Squared Error (MSE) and the match percentage of the model to the experimental data. Moreover, the model performance was compared with three common spring-dashpot models included the Maxwell model, the Burgers model and Standard Linear Solid model. Results affirmed that model E had the best data match in the whole mechanical loading and the MSE was considerably reduced. Subsequently model E was implemented for the tumor-included regions among the population study. Results showed that with a high match percentage, coefficients had significant deviations from the corresponding healthy regions' values for every individual. Consequently, personalized model E can be used for the healthy tissue characterization and tumor detection.

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  • Artificial tactile sensing has also been used for assessment of liver, brain, and submucosal tumors.

    “Artificial tactile sensing has also been used for other applications including assessment of liver, brain, and submucosal tumors [45].”

    From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [45]

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Authors
Madani, N., & Mojra, A.
Affiliations
K. N. Toosi University of Technology (IR)
Corresponding author
Mojra, A.
ORCID
Venue
Journal of the Mechanical Behavior of Biomedical Materials
Publisher
Elsevier BV
Published
2017-04
Volume
68
Pages
180-187
ISSN
1751-6161 (ISSN-L)
DOI
10.1016/j.jmbbm.2017.01.044
OpenAlex
W2585605954
Cited by
21
References
24
Citations by year
2025: 3 · 2024: 3 · 2023: 3 · 2022: 1 · 2021: 2 · 2020: 4 · 2019: 1 · 2018: 3 · 2017: 1
FWCI
1.11
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Elasticity and Material Modeling
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Ultrasound Imaging and Elastography
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Cellular Mechanics and Interactions
Keywords
breast tumors, viscoelastic behavior, compressive strain, spring-dashpot model, tumor detection
MeSH terms
Adult, Breast, Breast Neoplasms, Elasticity, Female, Humans, Middle Aged, Models, Biological, Robotics, Stress, Mechanical, Viscosity, Young Adult

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