Viscoelastic biomarker for differentiation of benign and malignant breast lesion in ultra- low frequency range.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
05 04 2019
Historique:
received: 14 03 2017
accepted: 15 03 2019
entrez: 7 4 2019
pubmed: 7 4 2019
medline: 21 10 2020
Statut: epublish

Résumé

Benign and malignant tumors differ in the viscoelastic properties of their cellular microenvironments and in their spatiotemporal response to very low frequency stimuli. These differences can introduce a unique viscoelastic biomarker in differentiation of benign and malignant tumors. This biomarker may reduce the number of unnecessary biopsies in breast patients. Although different methods have been developed so far for this purpose, none of them have focused on in vivo and in situ assessment of local viscoelastic properties in the ultra-low (sub-Hertz) frequency range. Here we introduce a new, noninvasive model-free method called Loss Angle Mapping (LAM). We assessed the performance results on 156 breast patients. The method was further improved by detection of out-of-plane motion using motion compensation cross correlation method (MCCC). 45 patients met this MCCC criterion and were considered for data analysis. Among this population, we found 77.8% sensitivity and 96.3% specificity (p < 0.0001) in discriminating between benign and malignant tumors using logistic regression method regarding the pre known information about the BIRADS number and size. The accuracy and area under the ROC curve, AUC, was 88.9% and 0.94, respectively. This method opens new avenues to investigate the mechanobiology behavior of different tissues in a frequency range that has not yet been explored in any in vivo patient studies.

Identifiants

pubmed: 30952880
doi: 10.1038/s41598-019-41885-9
pii: 10.1038/s41598-019-41885-9
pmc: PMC6450913
doi:

Substances chimiques

Biomarkers, Tumor 0

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

5737

Subventions

Organisme : NCI NIH HHS
ID : R01 CA168575
Pays : United States

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Auteurs

Alireza Nabavizadeh (A)

Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
Biomedical Informatics and Computational Biology, University of Minnesota Rochester, Rochester, Minnesota, USA.

Mahdi Bayat (M)

Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.

Viksit Kumar (V)

Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.

Adriana Gregory (A)

Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.

Jeremy Webb (J)

Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.

Azra Alizad (A)

Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
Department of Radiology, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.

Mostafa Fatemi (M)

Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine, Rochester, Minnesota, USA. Fatemi.mostafa@mayo.edu.

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