Evaluation of LIBRA Software for Fully Automated Mammographic Density Assessment in Breast Cancer Risk Prediction

Background The associations of density measures from the publicly available Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) software with breast cancer have primarily focused on estimates from the contralateral breast at the time of diagnosis. Purpose To evaluate LIBRA measures...

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Veröffentlicht in:Radiology 2020-07, Vol.296 (1), p.24-31
Hauptverfasser: Gastounioti, Aimilia, Kasi, Christine Damases, Scott, Christopher G, Brandt, Kathleen R, Jensen, Matthew R, Hruska, Carrie B, Wu, Fang F, Norman, Aaron D, Conant, Emily F, Winham, Stacey J, Kerlikowske, Karla, Kontos, Despina, Vachon, Celine M
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container_end_page 31
container_issue 1
container_start_page 24
container_title Radiology
container_volume 296
creator Gastounioti, Aimilia
Kasi, Christine Damases
Scott, Christopher G
Brandt, Kathleen R
Jensen, Matthew R
Hruska, Carrie B
Wu, Fang F
Norman, Aaron D
Conant, Emily F
Winham, Stacey J
Kerlikowske, Karla
Kontos, Despina
Vachon, Celine M
description Background The associations of density measures from the publicly available Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) software with breast cancer have primarily focused on estimates from the contralateral breast at the time of diagnosis. Purpose To evaluate LIBRA measures on mammograms obtained before breast cancer diagnosis and compare their performance to established density measures. Materials and Methods For this retrospective case-control study, full-field digital mammograms in for-processing (raw) and for-presentation (processed) formats were obtained (March 2008 to December 2011) in women who developed breast cancer an average of 2 years later and in age-matched control patients. LIBRA measures included absolute dense area and area percent density (PD) from both image formats. For comparison, dense area and PD were assessed by using the research software (Cumulus), and volumetric PD (VPD) and absolute dense volume were estimated with a commercially available software (Volpara). Density measures were compared by using Spearman correlation coefficients ( ), and conditional logistic regression (odds ratios [ORs] and 95% confidence intervals [CIs]) was performed to examine the associations of density measures with breast cancer by adjusting for age and body mass index. Results Evaluated were 437 women diagnosed with breast cancer (median age, 62 years ± 17 [standard deviation]) and 1225 matched control patients (median age, 61 years ± 16). LIBRA PD showed strong correlations with Cumulus PD ( = 0.77-0.84) and Volpara VPD ( = 0.85-0.90) ( < .001 for both). For LIBRA, the strongest breast cancer association was observed for PD from processed images (OR, 1.3; 95% CI: 1.1, 1.5), although the PD association from raw images was not significantly different (OR, 1.2; 95% CI: 1.1, 1.4; = .25). Slightly stronger breast cancer associations were seen for Cumulus PD (OR, 1.5; 95% CI: 1.3, 1.8; processed images; = .01) and Volpara VPD (OR, 1.4; 95% CI: 1.2, 1.7; raw images; = .004) compared with LIBRA measures. Conclusion Automated density measures provided by the Laboratory for Individualized Breast Radiodensity Assessment from raw and processed mammograms correlated with established area and volumetric density measures and showed comparable breast cancer associations. © RSNA, 2020
doi_str_mv 10.1148/radiol.2020192509
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Purpose To evaluate LIBRA measures on mammograms obtained before breast cancer diagnosis and compare their performance to established density measures. Materials and Methods For this retrospective case-control study, full-field digital mammograms in for-processing (raw) and for-presentation (processed) formats were obtained (March 2008 to December 2011) in women who developed breast cancer an average of 2 years later and in age-matched control patients. LIBRA measures included absolute dense area and area percent density (PD) from both image formats. For comparison, dense area and PD were assessed by using the research software (Cumulus), and volumetric PD (VPD) and absolute dense volume were estimated with a commercially available software (Volpara). Density measures were compared by using Spearman correlation coefficients ( ), and conditional logistic regression (odds ratios [ORs] and 95% confidence intervals [CIs]) was performed to examine the associations of density measures with breast cancer by adjusting for age and body mass index. Results Evaluated were 437 women diagnosed with breast cancer (median age, 62 years ± 17 [standard deviation]) and 1225 matched control patients (median age, 61 years ± 16). LIBRA PD showed strong correlations with Cumulus PD ( = 0.77-0.84) and Volpara VPD ( = 0.85-0.90) ( &lt; .001 for both). For LIBRA, the strongest breast cancer association was observed for PD from processed images (OR, 1.3; 95% CI: 1.1, 1.5), although the PD association from raw images was not significantly different (OR, 1.2; 95% CI: 1.1, 1.4; = .25). Slightly stronger breast cancer associations were seen for Cumulus PD (OR, 1.5; 95% CI: 1.3, 1.8; processed images; = .01) and Volpara VPD (OR, 1.4; 95% CI: 1.2, 1.7; raw images; = .004) compared with LIBRA measures. Conclusion Automated density measures provided by the Laboratory for Individualized Breast Radiodensity Assessment from raw and processed mammograms correlated with established area and volumetric density measures and showed comparable breast cancer associations. © RSNA, 2020</description><identifier>ISSN: 0033-8419</identifier><identifier>EISSN: 1527-1315</identifier><identifier>DOI: 10.1148/radiol.2020192509</identifier><identifier>PMID: 32396041</identifier><language>eng</language><publisher>United States: Radiological Society of North America</publisher><subject>Aged ; Breast - diagnostic imaging ; Breast Density ; Breast Neoplasms - diagnostic imaging ; Case-Control Studies ; Female ; Humans ; Mammography - methods ; Middle Aged ; Original Research ; Radiographic Image Interpretation, Computer-Assisted - methods ; Retrospective Studies ; Risk Factors ; Software</subject><ispartof>Radiology, 2020-07, Vol.296 (1), p.24-31</ispartof><rights>2020 by the Radiological Society of North America, Inc. 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c465t-e055c2538c4cf557a04b4a1d3b7a00b08b96132cd32fbf0f36c17fd68c1c0e363</citedby><cites>FETCH-LOGICAL-c465t-e055c2538c4cf557a04b4a1d3b7a00b08b96132cd32fbf0f36c17fd68c1c0e363</cites><orcidid>0000-0003-1340-0647 ; 0000-0002-1962-9322 ; 0000-0002-8492-9102 ; 0000-0001-8793-8779 ; 0000-0001-9031-5126 ; 0000-0003-0626-7219 ; 0000-0002-3359-7195 ; 0000-0003-2144-261X ; 0000-0002-6195-5201 ; 0000-0002-4208-2708</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,4013,27922,27923</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32396041$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gastounioti, Aimilia</creatorcontrib><creatorcontrib>Kasi, Christine Damases</creatorcontrib><creatorcontrib>Scott, Christopher G</creatorcontrib><creatorcontrib>Brandt, Kathleen R</creatorcontrib><creatorcontrib>Jensen, Matthew R</creatorcontrib><creatorcontrib>Hruska, Carrie B</creatorcontrib><creatorcontrib>Wu, Fang F</creatorcontrib><creatorcontrib>Norman, Aaron D</creatorcontrib><creatorcontrib>Conant, Emily F</creatorcontrib><creatorcontrib>Winham, Stacey J</creatorcontrib><creatorcontrib>Kerlikowske, Karla</creatorcontrib><creatorcontrib>Kontos, Despina</creatorcontrib><creatorcontrib>Vachon, Celine M</creatorcontrib><title>Evaluation of LIBRA Software for Fully Automated Mammographic Density Assessment in Breast Cancer Risk Prediction</title><title>Radiology</title><addtitle>Radiology</addtitle><description>Background The associations of density measures from the publicly available Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) software with breast cancer have primarily focused on estimates from the contralateral breast at the time of diagnosis. Purpose To evaluate LIBRA measures on mammograms obtained before breast cancer diagnosis and compare their performance to established density measures. Materials and Methods For this retrospective case-control study, full-field digital mammograms in for-processing (raw) and for-presentation (processed) formats were obtained (March 2008 to December 2011) in women who developed breast cancer an average of 2 years later and in age-matched control patients. LIBRA measures included absolute dense area and area percent density (PD) from both image formats. For comparison, dense area and PD were assessed by using the research software (Cumulus), and volumetric PD (VPD) and absolute dense volume were estimated with a commercially available software (Volpara). Density measures were compared by using Spearman correlation coefficients ( ), and conditional logistic regression (odds ratios [ORs] and 95% confidence intervals [CIs]) was performed to examine the associations of density measures with breast cancer by adjusting for age and body mass index. Results Evaluated were 437 women diagnosed with breast cancer (median age, 62 years ± 17 [standard deviation]) and 1225 matched control patients (median age, 61 years ± 16). LIBRA PD showed strong correlations with Cumulus PD ( = 0.77-0.84) and Volpara VPD ( = 0.85-0.90) ( &lt; .001 for both). For LIBRA, the strongest breast cancer association was observed for PD from processed images (OR, 1.3; 95% CI: 1.1, 1.5), although the PD association from raw images was not significantly different (OR, 1.2; 95% CI: 1.1, 1.4; = .25). Slightly stronger breast cancer associations were seen for Cumulus PD (OR, 1.5; 95% CI: 1.3, 1.8; processed images; = .01) and Volpara VPD (OR, 1.4; 95% CI: 1.2, 1.7; raw images; = .004) compared with LIBRA measures. Conclusion Automated density measures provided by the Laboratory for Individualized Breast Radiodensity Assessment from raw and processed mammograms correlated with established area and volumetric density measures and showed comparable breast cancer associations. © RSNA, 2020</description><subject>Aged</subject><subject>Breast - diagnostic imaging</subject><subject>Breast Density</subject><subject>Breast Neoplasms - diagnostic imaging</subject><subject>Case-Control Studies</subject><subject>Female</subject><subject>Humans</subject><subject>Mammography - methods</subject><subject>Middle Aged</subject><subject>Original Research</subject><subject>Radiographic Image Interpretation, Computer-Assisted - methods</subject><subject>Retrospective Studies</subject><subject>Risk Factors</subject><subject>Software</subject><issn>0033-8419</issn><issn>1527-1315</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVUUlP3DAUtqqiMtD-gF4qH3sJvOclywVpGFZpqla0PVuOY4NLEg-2A-LfN2hY2tN70rdKHyGfEQ4QRX0YdedDf8CAATZMQvOOLFCyqkCO8j1ZAHBe1AKbXbKX0h8AFLKuPpBdznhTgsAFuTu91_2ksw8jDY6uL4-vlvRncPlBR0tdiPRs6vtHupxyGHS2Hf2mhyFcR7258Yae2DH5PMMp2ZQGO2bqR3ocrU6ZrvRobKRXPt3SH9F23jzFfCQ7TvfJfnq---T32emv1UWx_n5-uVquCyNKmQsLUhomeW2EcVJWGkQrNHa8nV9ooW6bEjkzHWeudeB4abByXVkbNGB5yffJ0dZ3M7WD7czcLepebaIfdHxUQXv1PzL6G3Ud7lXFmSybZjb4-mwQw91kU1aDT8b2vR5tmJJiArBGLmo2U3FLNTGkFK17jUFQT1Op7VTqbapZ8-Xffq-Kl234X7eIktg</recordid><startdate>20200701</startdate><enddate>20200701</enddate><creator>Gastounioti, Aimilia</creator><creator>Kasi, Christine Damases</creator><creator>Scott, Christopher G</creator><creator>Brandt, Kathleen R</creator><creator>Jensen, Matthew R</creator><creator>Hruska, Carrie B</creator><creator>Wu, Fang F</creator><creator>Norman, Aaron D</creator><creator>Conant, Emily F</creator><creator>Winham, Stacey J</creator><creator>Kerlikowske, Karla</creator><creator>Kontos, Despina</creator><creator>Vachon, Celine M</creator><general>Radiological Society of North America</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-1340-0647</orcidid><orcidid>https://orcid.org/0000-0002-1962-9322</orcidid><orcidid>https://orcid.org/0000-0002-8492-9102</orcidid><orcidid>https://orcid.org/0000-0001-8793-8779</orcidid><orcidid>https://orcid.org/0000-0001-9031-5126</orcidid><orcidid>https://orcid.org/0000-0003-0626-7219</orcidid><orcidid>https://orcid.org/0000-0002-3359-7195</orcidid><orcidid>https://orcid.org/0000-0003-2144-261X</orcidid><orcidid>https://orcid.org/0000-0002-6195-5201</orcidid><orcidid>https://orcid.org/0000-0002-4208-2708</orcidid></search><sort><creationdate>20200701</creationdate><title>Evaluation of LIBRA Software for Fully Automated Mammographic Density Assessment in Breast Cancer Risk Prediction</title><author>Gastounioti, Aimilia ; Kasi, Christine Damases ; Scott, Christopher G ; Brandt, Kathleen R ; Jensen, Matthew R ; Hruska, Carrie B ; Wu, Fang F ; Norman, Aaron D ; Conant, Emily F ; Winham, Stacey J ; Kerlikowske, Karla ; Kontos, Despina ; Vachon, Celine M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c465t-e055c2538c4cf557a04b4a1d3b7a00b08b96132cd32fbf0f36c17fd68c1c0e363</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Aged</topic><topic>Breast - diagnostic imaging</topic><topic>Breast Density</topic><topic>Breast Neoplasms - diagnostic imaging</topic><topic>Case-Control Studies</topic><topic>Female</topic><topic>Humans</topic><topic>Mammography - methods</topic><topic>Middle Aged</topic><topic>Original Research</topic><topic>Radiographic Image Interpretation, Computer-Assisted - methods</topic><topic>Retrospective Studies</topic><topic>Risk Factors</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gastounioti, Aimilia</creatorcontrib><creatorcontrib>Kasi, Christine Damases</creatorcontrib><creatorcontrib>Scott, Christopher G</creatorcontrib><creatorcontrib>Brandt, Kathleen R</creatorcontrib><creatorcontrib>Jensen, Matthew R</creatorcontrib><creatorcontrib>Hruska, Carrie B</creatorcontrib><creatorcontrib>Wu, Fang F</creatorcontrib><creatorcontrib>Norman, Aaron D</creatorcontrib><creatorcontrib>Conant, Emily F</creatorcontrib><creatorcontrib>Winham, Stacey J</creatorcontrib><creatorcontrib>Kerlikowske, Karla</creatorcontrib><creatorcontrib>Kontos, Despina</creatorcontrib><creatorcontrib>Vachon, Celine M</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gastounioti, Aimilia</au><au>Kasi, Christine Damases</au><au>Scott, Christopher G</au><au>Brandt, Kathleen R</au><au>Jensen, Matthew R</au><au>Hruska, Carrie B</au><au>Wu, Fang F</au><au>Norman, Aaron D</au><au>Conant, Emily F</au><au>Winham, Stacey J</au><au>Kerlikowske, Karla</au><au>Kontos, Despina</au><au>Vachon, Celine M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation of LIBRA Software for Fully Automated Mammographic Density Assessment in Breast Cancer Risk Prediction</atitle><jtitle>Radiology</jtitle><addtitle>Radiology</addtitle><date>2020-07-01</date><risdate>2020</risdate><volume>296</volume><issue>1</issue><spage>24</spage><epage>31</epage><pages>24-31</pages><issn>0033-8419</issn><eissn>1527-1315</eissn><abstract>Background The associations of density measures from the publicly available Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) software with breast cancer have primarily focused on estimates from the contralateral breast at the time of diagnosis. Purpose To evaluate LIBRA measures on mammograms obtained before breast cancer diagnosis and compare their performance to established density measures. Materials and Methods For this retrospective case-control study, full-field digital mammograms in for-processing (raw) and for-presentation (processed) formats were obtained (March 2008 to December 2011) in women who developed breast cancer an average of 2 years later and in age-matched control patients. LIBRA measures included absolute dense area and area percent density (PD) from both image formats. For comparison, dense area and PD were assessed by using the research software (Cumulus), and volumetric PD (VPD) and absolute dense volume were estimated with a commercially available software (Volpara). Density measures were compared by using Spearman correlation coefficients ( ), and conditional logistic regression (odds ratios [ORs] and 95% confidence intervals [CIs]) was performed to examine the associations of density measures with breast cancer by adjusting for age and body mass index. Results Evaluated were 437 women diagnosed with breast cancer (median age, 62 years ± 17 [standard deviation]) and 1225 matched control patients (median age, 61 years ± 16). LIBRA PD showed strong correlations with Cumulus PD ( = 0.77-0.84) and Volpara VPD ( = 0.85-0.90) ( &lt; .001 for both). For LIBRA, the strongest breast cancer association was observed for PD from processed images (OR, 1.3; 95% CI: 1.1, 1.5), although the PD association from raw images was not significantly different (OR, 1.2; 95% CI: 1.1, 1.4; = .25). Slightly stronger breast cancer associations were seen for Cumulus PD (OR, 1.5; 95% CI: 1.3, 1.8; processed images; = .01) and Volpara VPD (OR, 1.4; 95% CI: 1.2, 1.7; raw images; = .004) compared with LIBRA measures. Conclusion Automated density measures provided by the Laboratory for Individualized Breast Radiodensity Assessment from raw and processed mammograms correlated with established area and volumetric density measures and showed comparable breast cancer associations. © RSNA, 2020</abstract><cop>United States</cop><pub>Radiological Society of North America</pub><pmid>32396041</pmid><doi>10.1148/radiol.2020192509</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0003-1340-0647</orcidid><orcidid>https://orcid.org/0000-0002-1962-9322</orcidid><orcidid>https://orcid.org/0000-0002-8492-9102</orcidid><orcidid>https://orcid.org/0000-0001-8793-8779</orcidid><orcidid>https://orcid.org/0000-0001-9031-5126</orcidid><orcidid>https://orcid.org/0000-0003-0626-7219</orcidid><orcidid>https://orcid.org/0000-0002-3359-7195</orcidid><orcidid>https://orcid.org/0000-0003-2144-261X</orcidid><orcidid>https://orcid.org/0000-0002-6195-5201</orcidid><orcidid>https://orcid.org/0000-0002-4208-2708</orcidid><oa>free_for_read</oa></addata></record>
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subjects Aged
Breast - diagnostic imaging
Breast Density
Breast Neoplasms - diagnostic imaging
Case-Control Studies
Female
Humans
Mammography - methods
Middle Aged
Original Research
Radiographic Image Interpretation, Computer-Assisted - methods
Retrospective Studies
Risk Factors
Software
title Evaluation of LIBRA Software for Fully Automated Mammographic Density Assessment in Breast Cancer Risk Prediction
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