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Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps

This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture-analysis techniques to differentiate non-invasively benign versus malignant breast lesions. Ultrasound B-mode images and radiofrequency data were acquired from...

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Autores principales: Sadeghi-Naini, Ali, Suraweera, Harini, Tran, William Tyler, Hadizad, Farnoosh, Bruni, Giancarlo, Rastegar, Rashin Fallah, Curpen, Belinda, Czarnota, Gregory J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5651882/
https://www.ncbi.nlm.nih.gov/pubmed/29057899
http://dx.doi.org/10.1038/s41598-017-13977-x
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author Sadeghi-Naini, Ali
Suraweera, Harini
Tran, William Tyler
Hadizad, Farnoosh
Bruni, Giancarlo
Rastegar, Rashin Fallah
Curpen, Belinda
Czarnota, Gregory J.
author_facet Sadeghi-Naini, Ali
Suraweera, Harini
Tran, William Tyler
Hadizad, Farnoosh
Bruni, Giancarlo
Rastegar, Rashin Fallah
Curpen, Belinda
Czarnota, Gregory J.
author_sort Sadeghi-Naini, Ali
collection PubMed
description This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture-analysis techniques to differentiate non-invasively benign versus malignant breast lesions. Ultrasound B-mode images and radiofrequency data were acquired from 78 patients with suspicious breast lesions. QUS spectral-analysis techniques were performed on radiofrequency data to generate parametric maps of mid-band fit, spectral slope, spectral intercept, spacing among scatterers, average scatterer diameter, and average acoustic concentration. Texture-analysis techniques were applied to determine imaging biomarkers consisting of mean, contrast, correlation, energy and homogeneity features of parametric maps. These biomarkers were utilized to classify benign versus malignant lesions with leave-one-patient-out cross-validation. Results were compared to histopathology findings from biopsy specimens and radiology reports on MR images to evaluate the accuracy of technique. Among the biomarkers investigated, one mean-value parameter and 14 textural features demonstrated statistically significant differences (p < 0.05) between the two lesion types. A hybrid biomarker developed using a stepwise feature selection method could classify the legions with a sensitivity of 96%, a specificity of 84%, and an AUC of 0.97. Findings from this study pave the way towards adapting novel QUS-based frameworks for breast cancer screening and rapid diagnosis in clinic.
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spelling pubmed-56518822017-10-26 Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps Sadeghi-Naini, Ali Suraweera, Harini Tran, William Tyler Hadizad, Farnoosh Bruni, Giancarlo Rastegar, Rashin Fallah Curpen, Belinda Czarnota, Gregory J. Sci Rep Article This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture-analysis techniques to differentiate non-invasively benign versus malignant breast lesions. Ultrasound B-mode images and radiofrequency data were acquired from 78 patients with suspicious breast lesions. QUS spectral-analysis techniques were performed on radiofrequency data to generate parametric maps of mid-band fit, spectral slope, spectral intercept, spacing among scatterers, average scatterer diameter, and average acoustic concentration. Texture-analysis techniques were applied to determine imaging biomarkers consisting of mean, contrast, correlation, energy and homogeneity features of parametric maps. These biomarkers were utilized to classify benign versus malignant lesions with leave-one-patient-out cross-validation. Results were compared to histopathology findings from biopsy specimens and radiology reports on MR images to evaluate the accuracy of technique. Among the biomarkers investigated, one mean-value parameter and 14 textural features demonstrated statistically significant differences (p < 0.05) between the two lesion types. A hybrid biomarker developed using a stepwise feature selection method could classify the legions with a sensitivity of 96%, a specificity of 84%, and an AUC of 0.97. Findings from this study pave the way towards adapting novel QUS-based frameworks for breast cancer screening and rapid diagnosis in clinic. Nature Publishing Group UK 2017-10-20 /pmc/articles/PMC5651882/ /pubmed/29057899 http://dx.doi.org/10.1038/s41598-017-13977-x Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Sadeghi-Naini, Ali
Suraweera, Harini
Tran, William Tyler
Hadizad, Farnoosh
Bruni, Giancarlo
Rastegar, Rashin Fallah
Curpen, Belinda
Czarnota, Gregory J.
Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps
title Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps
title_full Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps
title_fullStr Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps
title_full_unstemmed Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps
title_short Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps
title_sort breast-lesion characterization using textural features of quantitative ultrasound parametric maps
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5651882/
https://www.ncbi.nlm.nih.gov/pubmed/29057899
http://dx.doi.org/10.1038/s41598-017-13977-x
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