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Evaluation of breast parenchymal density with QUANTRA software

PURPOSE: To evaluate breast parenchymal density using QUANTRA software and to correlate numerical breast density values obtained from QUANTRA with ACR BI-RADS breast density categories. MATERIALS AND METHODS: Two-view digital mammograms of 545 consecutive women (mean age - 47.7 years) were categoriz...

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Autores principales: Pahwa, Shivani, Hari, Smriti, Thulkar, Sanjay, Angraal, Suveen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Medknow Publications & Media Pvt Ltd 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4693388/
https://www.ncbi.nlm.nih.gov/pubmed/26752820
http://dx.doi.org/10.4103/0971-3026.169458
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author Pahwa, Shivani
Hari, Smriti
Thulkar, Sanjay
Angraal, Suveen
author_facet Pahwa, Shivani
Hari, Smriti
Thulkar, Sanjay
Angraal, Suveen
author_sort Pahwa, Shivani
collection PubMed
description PURPOSE: To evaluate breast parenchymal density using QUANTRA software and to correlate numerical breast density values obtained from QUANTRA with ACR BI-RADS breast density categories. MATERIALS AND METHODS: Two-view digital mammograms of 545 consecutive women (mean age - 47.7 years) were categorized visually by three independent radiologists into one of the four ACR BI-RADS categories (D1-D4). Numerical breast density values as obtained by QUANTRA software were then used to establish the cutoff values for each category using receiver operator characteristic (ROC) analysis. RESULTS: Numerical breast density values obtained by QUANTRA (range - 7-42%) were systematically lower than visual estimates. QUANTRA breast density value of less than 14.5% could accurately differentiate category D1 from the categories D2, D3, and D4 [area under curve (AUC) on ROC analysis - 94.09%, sensitivity - 85.71%, specificity - 84.21%]. QUANTRA density values of <19.5% accurately differentiated categories D1 and D2 from D3 and D4 (AUC - 94.4%, sensitivity - 87.50%, specificity - 84.60%); QUANTRA density values of <26.5% accurately differentiated categories D1, D2, and D3 from category D4 (AUC - 90.75%, sensitivity - 88.89%, specificity - 88.621%). CONCLUSIONS: Breast density values obtained by QUANTRA software can be used to obtain objective cutoff values for each ACR BI-RADS breast density category. Although the numerical density values obtained by QUANTRA are lower than visual estimates, they correlate well with the BI-RADS breast density categories assigned visually to the mammograms.
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spelling pubmed-46933882016-01-08 Evaluation of breast parenchymal density with QUANTRA software Pahwa, Shivani Hari, Smriti Thulkar, Sanjay Angraal, Suveen Indian J Radiol Imaging Breast Radiology PURPOSE: To evaluate breast parenchymal density using QUANTRA software and to correlate numerical breast density values obtained from QUANTRA with ACR BI-RADS breast density categories. MATERIALS AND METHODS: Two-view digital mammograms of 545 consecutive women (mean age - 47.7 years) were categorized visually by three independent radiologists into one of the four ACR BI-RADS categories (D1-D4). Numerical breast density values as obtained by QUANTRA software were then used to establish the cutoff values for each category using receiver operator characteristic (ROC) analysis. RESULTS: Numerical breast density values obtained by QUANTRA (range - 7-42%) were systematically lower than visual estimates. QUANTRA breast density value of less than 14.5% could accurately differentiate category D1 from the categories D2, D3, and D4 [area under curve (AUC) on ROC analysis - 94.09%, sensitivity - 85.71%, specificity - 84.21%]. QUANTRA density values of <19.5% accurately differentiated categories D1 and D2 from D3 and D4 (AUC - 94.4%, sensitivity - 87.50%, specificity - 84.60%); QUANTRA density values of <26.5% accurately differentiated categories D1, D2, and D3 from category D4 (AUC - 90.75%, sensitivity - 88.89%, specificity - 88.621%). CONCLUSIONS: Breast density values obtained by QUANTRA software can be used to obtain objective cutoff values for each ACR BI-RADS breast density category. Although the numerical density values obtained by QUANTRA are lower than visual estimates, they correlate well with the BI-RADS breast density categories assigned visually to the mammograms. Medknow Publications & Media Pvt Ltd 2015 /pmc/articles/PMC4693388/ /pubmed/26752820 http://dx.doi.org/10.4103/0971-3026.169458 Text en Copyright: © Indian Journal of Radiology and Imaging http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.
spellingShingle Breast Radiology
Pahwa, Shivani
Hari, Smriti
Thulkar, Sanjay
Angraal, Suveen
Evaluation of breast parenchymal density with QUANTRA software
title Evaluation of breast parenchymal density with QUANTRA software
title_full Evaluation of breast parenchymal density with QUANTRA software
title_fullStr Evaluation of breast parenchymal density with QUANTRA software
title_full_unstemmed Evaluation of breast parenchymal density with QUANTRA software
title_short Evaluation of breast parenchymal density with QUANTRA software
title_sort evaluation of breast parenchymal density with quantra software
topic Breast Radiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4693388/
https://www.ncbi.nlm.nih.gov/pubmed/26752820
http://dx.doi.org/10.4103/0971-3026.169458
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