A Novel Automated Mammographic Density Measure and Breast Cancer Risk

Mammographic breast density is a strong breast cancer risk factor but is not used in the clinical setting, partly because of a lack of standardization and automation. We developed an automated and objective measurement of the grayscale value variation within a mammogram, evaluated its association wi...

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Veröffentlicht in:JNCI : Journal of the National Cancer Institute 2012-07, Vol.104 (13), p.1028-1037
Hauptverfasser: HEINE, John J, SCOTT, Christopher G, SHANE PANKRATZ, V, VACHON, Celine M, SELLERS, Thomas A, BRANDT, Kathleen R, SERIE, Daniel J, WU, Fang-Fang, MORTON, Marilyn J, SCHUELER, Beth A, COUCH, Fergus J, OLSON, Janet E
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container_end_page 1037
container_issue 13
container_start_page 1028
container_title JNCI : Journal of the National Cancer Institute
container_volume 104
creator HEINE, John J
SCOTT, Christopher G
SHANE PANKRATZ, V
VACHON, Celine M
SELLERS, Thomas A
BRANDT, Kathleen R
SERIE, Daniel J
WU, Fang-Fang
MORTON, Marilyn J
SCHUELER, Beth A
COUCH, Fergus J
OLSON, Janet E
description Mammographic breast density is a strong breast cancer risk factor but is not used in the clinical setting, partly because of a lack of standardization and automation. We developed an automated and objective measurement of the grayscale value variation within a mammogram, evaluated its association with breast cancer, and compared its performance with that of percent density (PD). Three clinic-based studies were included: a case-cohort study of 217 breast cancer case subjects and 2094 non-case subjects and two case-control studies comprising 928 case subjects and 1039 control subjects and 246 case subjects and 516 control subjects, respectively. Percent density was estimated from digitized mammograms using the computer-assisted Cumulus thresholding program, and variation was estimated from an automated algorithm. We estimated hazards ratios (HRs), odds ratios (ORs), the area under the receiver operating characteristic curve (AUC), and 95% confidence intervals (CIs) using Cox proportional hazards models for the cohort and logistic regression for case-control studies, with adjustment for age and body mass index. We performed a meta-analysis using random study effects to obtain pooled estimates of the associations between the two mammographic measures and breast cancer. All statistical tests were two-sided. The variation measure was statistically significantly associated with the risk of breast cancer in all three studies (highest vs lowest quartile: HR = 2.0 [95% CI = 1.3 to 3.1]; OR = 2.7 [95% CI = 2.1 to 3.6]; OR = 2.4 [95% CI = 1.4 to 3.9]; [corrected] all P (trend) < .001). [corrected]. The risk estimates and AUCs for the variation measure were similar to [corrected] those for percent density (AUCs for variation = 0.60-0.62 and [corrected] AUCs for percent density = 0.61-0.65). [corrected]. A meta-analysis of the three studies demonstrated similar associations [corrected] between variation and breast cancer (highest vs lowest quartile: RR = 1.8, 95% CI = 1.4 to 2.3) and [corrected] percent density and breast cancer (highest vs lowest quartile: RR = 2.3, 95% CI = 1.9 to 2.9). The association between the automated variation measure and the risk of breast cancer is at least as strong as that for percent density. Efforts to further evaluate and translate the variation measure to the clinical setting are warranted.
doi_str_mv 10.1093/jnci/djs254
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We developed an automated and objective measurement of the grayscale value variation within a mammogram, evaluated its association with breast cancer, and compared its performance with that of percent density (PD). Three clinic-based studies were included: a case-cohort study of 217 breast cancer case subjects and 2094 non-case subjects and two case-control studies comprising 928 case subjects and 1039 control subjects and 246 case subjects and 516 control subjects, respectively. Percent density was estimated from digitized mammograms using the computer-assisted Cumulus thresholding program, and variation was estimated from an automated algorithm. We estimated hazards ratios (HRs), odds ratios (ORs), the area under the receiver operating characteristic curve (AUC), and 95% confidence intervals (CIs) using Cox proportional hazards models for the cohort and logistic regression for case-control studies, with adjustment for age and body mass index. We performed a meta-analysis using random study effects to obtain pooled estimates of the associations between the two mammographic measures and breast cancer. All statistical tests were two-sided. The variation measure was statistically significantly associated with the risk of breast cancer in all three studies (highest vs lowest quartile: HR = 2.0 [95% CI = 1.3 to 3.1]; OR = 2.7 [95% CI = 2.1 to 3.6]; OR = 2.4 [95% CI = 1.4 to 3.9]; [corrected] all P (trend) &lt; .001). [corrected]. The risk estimates and AUCs for the variation measure were similar to [corrected] those for percent density (AUCs for variation = 0.60-0.62 and [corrected] AUCs for percent density = 0.61-0.65). [corrected]. A meta-analysis of the three studies demonstrated similar associations [corrected] between variation and breast cancer (highest vs lowest quartile: RR = 1.8, 95% CI = 1.4 to 2.3) and [corrected] percent density and breast cancer (highest vs lowest quartile: RR = 2.3, 95% CI = 1.9 to 2.9). The association between the automated variation measure and the risk of breast cancer is at least as strong as that for percent density. Efforts to further evaluate and translate the variation measure to the clinical setting are warranted.</description><identifier>ISSN: 0027-8874</identifier><identifier>EISSN: 1460-2105</identifier><identifier>DOI: 10.1093/jnci/djs254</identifier><identifier>PMID: 22761274</identifier><identifier>CODEN: JNCIEQ</identifier><language>eng</language><publisher>Cary, NC: Oxford University Press</publisher><subject>Adult ; Age ; Aged ; Algorithms ; Area Under Curve ; Automation ; Biological and medical sciences ; Body mass ; Breast - pathology ; Breast cancer ; Breast Neoplasms - diagnostic imaging ; Breast Neoplasms - epidemiology ; Breast Neoplasms - pathology ; Cancer ; Case-Control Studies ; Cohort Studies ; Female ; Gynecology. Andrology. Obstetrics ; Humans ; Logistic Models ; Mammary gland diseases ; Mammography ; Mammography - methods ; Mammography - standards ; Mammography - trends ; Medical sciences ; Middle Aged ; Proportional Hazards Models ; Risk Factors ; ROC Curve ; Tumors ; Women</subject><ispartof>JNCI : Journal of the National Cancer Institute, 2012-07, Vol.104 (13), p.1028-1037</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright Oxford Publishing Limited(England) Jul 4, 2012</rights><rights>The Author 2012. Published by Oxford University Press. All rights reserved. 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We developed an automated and objective measurement of the grayscale value variation within a mammogram, evaluated its association with breast cancer, and compared its performance with that of percent density (PD). Three clinic-based studies were included: a case-cohort study of 217 breast cancer case subjects and 2094 non-case subjects and two case-control studies comprising 928 case subjects and 1039 control subjects and 246 case subjects and 516 control subjects, respectively. Percent density was estimated from digitized mammograms using the computer-assisted Cumulus thresholding program, and variation was estimated from an automated algorithm. We estimated hazards ratios (HRs), odds ratios (ORs), the area under the receiver operating characteristic curve (AUC), and 95% confidence intervals (CIs) using Cox proportional hazards models for the cohort and logistic regression for case-control studies, with adjustment for age and body mass index. We performed a meta-analysis using random study effects to obtain pooled estimates of the associations between the two mammographic measures and breast cancer. All statistical tests were two-sided. The variation measure was statistically significantly associated with the risk of breast cancer in all three studies (highest vs lowest quartile: HR = 2.0 [95% CI = 1.3 to 3.1]; OR = 2.7 [95% CI = 2.1 to 3.6]; OR = 2.4 [95% CI = 1.4 to 3.9]; [corrected] all P (trend) &lt; .001). [corrected]. The risk estimates and AUCs for the variation measure were similar to [corrected] those for percent density (AUCs for variation = 0.60-0.62 and [corrected] AUCs for percent density = 0.61-0.65). [corrected]. A meta-analysis of the three studies demonstrated similar associations [corrected] between variation and breast cancer (highest vs lowest quartile: RR = 1.8, 95% CI = 1.4 to 2.3) and [corrected] percent density and breast cancer (highest vs lowest quartile: RR = 2.3, 95% CI = 1.9 to 2.9). The association between the automated variation measure and the risk of breast cancer is at least as strong as that for percent density. Efforts to further evaluate and translate the variation measure to the clinical setting are warranted.</description><subject>Adult</subject><subject>Age</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Area Under Curve</subject><subject>Automation</subject><subject>Biological and medical sciences</subject><subject>Body mass</subject><subject>Breast - pathology</subject><subject>Breast cancer</subject><subject>Breast Neoplasms - diagnostic imaging</subject><subject>Breast Neoplasms - epidemiology</subject><subject>Breast Neoplasms - pathology</subject><subject>Cancer</subject><subject>Case-Control Studies</subject><subject>Cohort Studies</subject><subject>Female</subject><subject>Gynecology. Andrology. Obstetrics</subject><subject>Humans</subject><subject>Logistic Models</subject><subject>Mammary gland diseases</subject><subject>Mammography</subject><subject>Mammography - methods</subject><subject>Mammography - standards</subject><subject>Mammography - trends</subject><subject>Medical sciences</subject><subject>Middle Aged</subject><subject>Proportional Hazards Models</subject><subject>Risk Factors</subject><subject>ROC Curve</subject><subject>Tumors</subject><subject>Women</subject><issn>0027-8874</issn><issn>1460-2105</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpd0V1r2zAUBmAxNtY061Xvh6AMCsXr0Zdl3wyytPuAdIPRXgtZllqltpVJdqH_fgrJuna6EUIPR-foReiYwEcCNTtfD8aft-tEBX-FZoSXUFAC4jWaAVBZVJXkB-gwpTXkVVP-Fh1QKktCJZ-hywX-ER5shxfTGHo92hZf6b4Pt1Fv7rzBF3ZIfnzEV1anKVqshxZ_jvkw4qUejI34l0_379Abp7tkj_b7HN18ubxefitWP79-Xy5WheGSjoUrWy5ELaXknJTCCOoodQ4aAMYkbVzleJNbtyXnlRGcZcdJywwVAKap2Bx92tXdTE1vW2OHMepObaLvdXxUQXv18mbwd-o2PChWsvwyyQVO9wVi-D3ZNKreJ2O7Tg82TEkRYLSqagCZ6cl_dB2mOOTxtooBA0FZVmc7ZWJIKVr31AwBtY1HbeNRu3iyfv-8_yf7N48MPuyBTkZ3LuY_9umfKympJVTsD5n6lzU</recordid><startdate>20120703</startdate><enddate>20120703</enddate><creator>HEINE, John J</creator><creator>SCOTT, Christopher G</creator><creator>SHANE PANKRATZ, V</creator><creator>VACHON, Celine M</creator><creator>SELLERS, Thomas A</creator><creator>BRANDT, Kathleen R</creator><creator>SERIE, Daniel J</creator><creator>WU, Fang-Fang</creator><creator>MORTON, Marilyn J</creator><creator>SCHUELER, Beth A</creator><creator>COUCH, Fergus J</creator><creator>OLSON, Janet E</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>IQODW</scope><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>7TO</scope><scope>7U7</scope><scope>7U9</scope><scope>C1K</scope><scope>H94</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7T2</scope><scope>7U1</scope><scope>7U2</scope><scope>5PM</scope></search><sort><creationdate>20120703</creationdate><title>A Novel Automated Mammographic Density Measure and Breast Cancer Risk</title><author>HEINE, John J ; SCOTT, Christopher G ; SHANE PANKRATZ, V ; VACHON, Celine M ; SELLERS, Thomas A ; BRANDT, Kathleen R ; SERIE, Daniel J ; WU, Fang-Fang ; MORTON, Marilyn J ; SCHUELER, Beth A ; COUCH, Fergus J ; OLSON, Janet E</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c472t-f6d455977744165c52f22ff0b003372bf8f4b105e6448c54374441d3c2500cb83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Adult</topic><topic>Age</topic><topic>Aged</topic><topic>Algorithms</topic><topic>Area Under Curve</topic><topic>Automation</topic><topic>Biological and medical sciences</topic><topic>Body mass</topic><topic>Breast - pathology</topic><topic>Breast cancer</topic><topic>Breast Neoplasms - diagnostic imaging</topic><topic>Breast Neoplasms - epidemiology</topic><topic>Breast Neoplasms - pathology</topic><topic>Cancer</topic><topic>Case-Control Studies</topic><topic>Cohort Studies</topic><topic>Female</topic><topic>Gynecology. 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Obstetrics</topic><topic>Humans</topic><topic>Logistic Models</topic><topic>Mammary gland diseases</topic><topic>Mammography</topic><topic>Mammography - methods</topic><topic>Mammography - standards</topic><topic>Mammography - trends</topic><topic>Medical sciences</topic><topic>Middle Aged</topic><topic>Proportional Hazards Models</topic><topic>Risk Factors</topic><topic>ROC Curve</topic><topic>Tumors</topic><topic>Women</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>HEINE, John J</creatorcontrib><creatorcontrib>SCOTT, Christopher G</creatorcontrib><creatorcontrib>SHANE PANKRATZ, V</creatorcontrib><creatorcontrib>VACHON, Celine M</creatorcontrib><creatorcontrib>SELLERS, Thomas A</creatorcontrib><creatorcontrib>BRANDT, Kathleen R</creatorcontrib><creatorcontrib>SERIE, Daniel J</creatorcontrib><creatorcontrib>WU, Fang-Fang</creatorcontrib><creatorcontrib>MORTON, Marilyn J</creatorcontrib><creatorcontrib>SCHUELER, Beth A</creatorcontrib><creatorcontrib>COUCH, Fergus J</creatorcontrib><creatorcontrib>OLSON, Janet E</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Nursing &amp; Allied Health Premium</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Risk Abstracts</collection><collection>Safety Science and Risk</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>JNCI : Journal of the National Cancer Institute</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>HEINE, John J</au><au>SCOTT, Christopher G</au><au>SHANE PANKRATZ, V</au><au>VACHON, Celine M</au><au>SELLERS, Thomas A</au><au>BRANDT, Kathleen R</au><au>SERIE, Daniel J</au><au>WU, Fang-Fang</au><au>MORTON, Marilyn J</au><au>SCHUELER, Beth A</au><au>COUCH, Fergus J</au><au>OLSON, Janet E</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Novel Automated Mammographic Density Measure and Breast Cancer Risk</atitle><jtitle>JNCI : Journal of the National Cancer Institute</jtitle><addtitle>J Natl Cancer Inst</addtitle><date>2012-07-03</date><risdate>2012</risdate><volume>104</volume><issue>13</issue><spage>1028</spage><epage>1037</epage><pages>1028-1037</pages><issn>0027-8874</issn><eissn>1460-2105</eissn><coden>JNCIEQ</coden><abstract>Mammographic breast density is a strong breast cancer risk factor but is not used in the clinical setting, partly because of a lack of standardization and automation. We developed an automated and objective measurement of the grayscale value variation within a mammogram, evaluated its association with breast cancer, and compared its performance with that of percent density (PD). Three clinic-based studies were included: a case-cohort study of 217 breast cancer case subjects and 2094 non-case subjects and two case-control studies comprising 928 case subjects and 1039 control subjects and 246 case subjects and 516 control subjects, respectively. Percent density was estimated from digitized mammograms using the computer-assisted Cumulus thresholding program, and variation was estimated from an automated algorithm. We estimated hazards ratios (HRs), odds ratios (ORs), the area under the receiver operating characteristic curve (AUC), and 95% confidence intervals (CIs) using Cox proportional hazards models for the cohort and logistic regression for case-control studies, with adjustment for age and body mass index. We performed a meta-analysis using random study effects to obtain pooled estimates of the associations between the two mammographic measures and breast cancer. All statistical tests were two-sided. The variation measure was statistically significantly associated with the risk of breast cancer in all three studies (highest vs lowest quartile: HR = 2.0 [95% CI = 1.3 to 3.1]; OR = 2.7 [95% CI = 2.1 to 3.6]; OR = 2.4 [95% CI = 1.4 to 3.9]; [corrected] all P (trend) &lt; .001). [corrected]. The risk estimates and AUCs for the variation measure were similar to [corrected] those for percent density (AUCs for variation = 0.60-0.62 and [corrected] AUCs for percent density = 0.61-0.65). [corrected]. A meta-analysis of the three studies demonstrated similar associations [corrected] between variation and breast cancer (highest vs lowest quartile: RR = 1.8, 95% CI = 1.4 to 2.3) and [corrected] percent density and breast cancer (highest vs lowest quartile: RR = 2.3, 95% CI = 1.9 to 2.9). The association between the automated variation measure and the risk of breast cancer is at least as strong as that for percent density. Efforts to further evaluate and translate the variation measure to the clinical setting are warranted.</abstract><cop>Cary, NC</cop><pub>Oxford University Press</pub><pmid>22761274</pmid><doi>10.1093/jnci/djs254</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record>
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source MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Oxford University Press Journals All Titles (1996-Current); Alma/SFX Local Collection
subjects Adult
Age
Aged
Algorithms
Area Under Curve
Automation
Biological and medical sciences
Body mass
Breast - pathology
Breast cancer
Breast Neoplasms - diagnostic imaging
Breast Neoplasms - epidemiology
Breast Neoplasms - pathology
Cancer
Case-Control Studies
Cohort Studies
Female
Gynecology. Andrology. Obstetrics
Humans
Logistic Models
Mammary gland diseases
Mammography
Mammography - methods
Mammography - standards
Mammography - trends
Medical sciences
Middle Aged
Proportional Hazards Models
Risk Factors
ROC Curve
Tumors
Women
title A Novel Automated Mammographic Density Measure and Breast Cancer Risk
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