Evidence map›Paper›PMID 19306059›Full record

Trial reportBreast cancer research and treatment2009

Differentiation of benign and malignant breast lesions by mechanical imaging.

Vladimir Egorov, Thomas Kearney, Stanley B Pollak, Chand Rohatgi, Noune Sarvazyan, Suren Airapetian, Stephanie Browning, Armen Sarvazyan

Abstract readClinical TrialComparative StudyMulticenter Study
In one paragraph

Trial report in Breast cancer research and treatment, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Vaginal tactile imaging: A review.Pelviperineology · 2023
    Article
  4. Finite element model of mechanical imaging of the breast.Journal of medical imaging (Bellingham, Wash.) · 2022
    Article
  5. Use of Palpation Imaging in Diagnosis of Breast Diseases: A Way to Improve the Detection Rate.Medical science monitor : international medical journal of experimental and clinical research · 2020
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Vaginal tactile imaging.IEEE transactions on bio-medical engineering · 2010
    Article
  16. Mechanical imaging of the breast.IEEE transactions on medical imaging · 2008
    Article
  17. 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

8 authors.

Vladimir EgorovArtann Laboratories, 1459 Lower Ferry Rd, Trenton, NJ, 08618, USA. vegorov@artannlabs.com
Thomas Kearney
Stanley B Pollak
Chand Rohatgi
Noune Sarvazyan
Suren Airapetian
Stephanie Browning
Armen Sarvazyan

Funding

IMAGING NETWORK FOR BREAST CANCER MASS SCREENINGR44CA091392 · NCI · ARTANN LABORATORIES, INC. · PI SARVAZYAN, ARMEN · 2004 to 2007
$2.2M
IMAGING NETWORK FOR BREAST CANCER MASS SCREENINGR43CA091392 · NCI · ARTANN LABORATORIES, INC. · PI SARVAZYAN, ARMEN · 2001 to 2001
$146k
NCI NIH HHS CA091392NCI NIH HHS R43 CA091392NCI NIH HHS R44 CA091392
6 · The paper itself

Abstract

Mechanical imaging yields tissue elasticity map and provides quantitative characterization of a detected pathology. The changes in the surface stress patterns as a function of applied load provide information about the elastic composition and geometry of the underlying tissue structures. The objective of this study is the clinical evaluation of breast mechanical imager for breast lesion characterization and differentiation between benign and malignant lesions. The breast mechanical imager includes a probe with pressure sensor array, an electronic unit providing data acquisition from the pressure sensors and communication with a touch-screen laptop computer. We have developed an examination procedure and algorithms to provide assessment of breast lesion features such as hardness related parameters, mobility, and shape. A statistical Bayesian classifier was constructed to distinguish between benign and malignant lesions by utilizing all the listed features as the input. Clinical results for 179 cases, collected at four different clinical sites, have demonstrated that the breast mechanical imager provides a reliable image formation of breast tissue abnormalities and calculation of lesion features. Malignant breast lesions (histologically confirmed) demonstrated increased hardness and strain hardening as well as decreased mobility and longer boundary length in comparison with benign lesions. Statistical analysis of differentiation capability for 147 benign and 32 malignant lesions revealed an average sensitivity of 91.4% and specificity of 86.8% with a standard deviation of +/-6.1%. The area under the receiver operating characteristic curve characterizing benign and malignant lesion discrimination is 86.1% with the confidence interval ranging from 80.3 to 90.9%, with a significance level of P = 0.0001 (area = 50%). The multisite clinical study demonstrated the capability of mechanical imaging for characterization and differentiation of benign and malignant breast lesions. We hypothesize that the breast mechanical imager has the potential to be used as a cost effective device for cancer diagnostics that could reduce the benign biopsy rate, serve as an adjunct to mammography and to be utilized as a screening device for breast cancer detection.

Indexed as

PressureStress, MechanicalAdultAgedAlgorithmsBiopsyBreast CystBreast DiseasesBreast NeoplasmsCarcinomaDiagnosis, DifferentialFemaleFibroadenomaHardnessHumansImage Interpretation, Computer-Assisted

Identifiers

PMID19306059
PMCPMC2803347

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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.