A novel automated mammographic density measure and breast cancer risk

John J. Heine, Christopher G. Scott, Thomas A. Sellers, Kathleen R Brandt, Daniel J. Serie, Fang Fang Wu, Marilyn J. Morton, Beth A. Schueler, Fergus J Couch, Janet E Olson, V. Shane Pankratz, Celine M Vachon

Research output: Contribution to journalArticle

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Abstract

Background 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).Methods Three clinic-based studies were included: a casecohort study of 217 breast cancer case subjects and 2094 non-case subjects and two casecontrol 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 casecontrol 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.Results The variation measure was statistically significantly associated with the risk of breast cancer in all three studies (highest vs lowest quartile: HR = 7.0 [95% CI = 4.6 to 10.4]; OR = 10.7 [95% CI = 7.5 to 15.3]; OR = 2.6 [95% CI = 1.6 to 4.2]; all Ptrend <. 001). In two studies, the risk estimates and AUCs for the variation measure were greater than those for percent density (AUCs for variation = 0.71 and 0.76; AUCs for percent density = 0.65 and 0.65), whereas in the third study, these estimates were similar (AUC for variation = 0.60 and AUC for percent density = 0.61). A meta-analysis of the three studies demonstrated a stronger association between variation and breast cancer (highest vs lowest quartile: RR = 3.6, 95% CI = 1.9 to 7.0) than between percent density and breast cancer (highest vs lowest quartile: RR = 2.3, 95% CI = 1.9 to 2.9).Conclusion 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.

Original languageEnglish (US)
Pages (from-to)1028-1037
Number of pages10
JournalJournal of the National Cancer Institute
Volume104
Issue number13
DOIs
StatePublished - Jul 3 2012

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Breast Neoplasms
Area Under Curve
Confidence Intervals
Odds Ratio
Meta-Analysis
Breast Density
Automation
Proportional Hazards Models
ROC Curve
Body Mass Index
Logistic Models

ASJC Scopus subject areas

  • Cancer Research
  • Oncology

Cite this

A novel automated mammographic density measure and breast cancer risk. / Heine, John J.; Scott, Christopher G.; Sellers, Thomas A.; Brandt, Kathleen R; Serie, Daniel J.; Wu, Fang Fang; Morton, Marilyn J.; Schueler, Beth A.; Couch, Fergus J; Olson, Janet E; Pankratz, V. Shane; Vachon, Celine M.

In: Journal of the National Cancer Institute, Vol. 104, No. 13, 03.07.2012, p. 1028-1037.

Research output: Contribution to journalArticle

Heine, JJ, Scott, CG, Sellers, TA, Brandt, KR, Serie, DJ, Wu, FF, Morton, MJ, Schueler, BA, Couch, FJ, Olson, JE, Pankratz, VS & Vachon, CM 2012, 'A novel automated mammographic density measure and breast cancer risk', Journal of the National Cancer Institute, vol. 104, no. 13, pp. 1028-1037. https://doi.org/10.1093/jnci/djs254
Heine, John J. ; Scott, Christopher G. ; Sellers, Thomas A. ; Brandt, Kathleen R ; Serie, Daniel J. ; Wu, Fang Fang ; Morton, Marilyn J. ; Schueler, Beth A. ; Couch, Fergus J ; Olson, Janet E ; Pankratz, V. Shane ; Vachon, Celine M. / A novel automated mammographic density measure and breast cancer risk. In: Journal of the National Cancer Institute. 2012 ; Vol. 104, No. 13. pp. 1028-1037.
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title = "A novel automated mammographic density measure and breast cancer risk",
abstract = "Background 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).Methods Three clinic-based studies were included: a casecohort study of 217 breast cancer case subjects and 2094 non-case subjects and two casecontrol 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 casecontrol 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.Results The variation measure was statistically significantly associated with the risk of breast cancer in all three studies (highest vs lowest quartile: HR = 7.0 [95{\%} CI = 4.6 to 10.4]; OR = 10.7 [95{\%} CI = 7.5 to 15.3]; OR = 2.6 [95{\%} CI = 1.6 to 4.2]; all Ptrend <. 001). In two studies, the risk estimates and AUCs for the variation measure were greater than those for percent density (AUCs for variation = 0.71 and 0.76; AUCs for percent density = 0.65 and 0.65), whereas in the third study, these estimates were similar (AUC for variation = 0.60 and AUC for percent density = 0.61). A meta-analysis of the three studies demonstrated a stronger association between variation and breast cancer (highest vs lowest quartile: RR = 3.6, 95{\%} CI = 1.9 to 7.0) than between percent density and breast cancer (highest vs lowest quartile: RR = 2.3, 95{\%} CI = 1.9 to 2.9).Conclusion 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.",
author = "Heine, {John J.} and Scott, {Christopher G.} and Sellers, {Thomas A.} and Brandt, {Kathleen R} and Serie, {Daniel J.} and Wu, {Fang Fang} and Morton, {Marilyn J.} and Schueler, {Beth A.} and Couch, {Fergus J} and Olson, {Janet E} and Pankratz, {V. Shane} and Vachon, {Celine M}",
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T1 - A novel automated mammographic density measure and breast cancer risk

AU - Heine, John J.

AU - Scott, Christopher G.

AU - Sellers, Thomas A.

AU - Brandt, Kathleen R

AU - Serie, Daniel J.

AU - Wu, Fang Fang

AU - Morton, Marilyn J.

AU - Schueler, Beth A.

AU - Couch, Fergus J

AU - Olson, Janet E

AU - Pankratz, V. Shane

AU - Vachon, Celine M

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N2 - Background 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).Methods Three clinic-based studies were included: a casecohort study of 217 breast cancer case subjects and 2094 non-case subjects and two casecontrol 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 casecontrol 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.Results The variation measure was statistically significantly associated with the risk of breast cancer in all three studies (highest vs lowest quartile: HR = 7.0 [95% CI = 4.6 to 10.4]; OR = 10.7 [95% CI = 7.5 to 15.3]; OR = 2.6 [95% CI = 1.6 to 4.2]; all Ptrend <. 001). In two studies, the risk estimates and AUCs for the variation measure were greater than those for percent density (AUCs for variation = 0.71 and 0.76; AUCs for percent density = 0.65 and 0.65), whereas in the third study, these estimates were similar (AUC for variation = 0.60 and AUC for percent density = 0.61). A meta-analysis of the three studies demonstrated a stronger association between variation and breast cancer (highest vs lowest quartile: RR = 3.6, 95% CI = 1.9 to 7.0) than between percent density and breast cancer (highest vs lowest quartile: RR = 2.3, 95% CI = 1.9 to 2.9).Conclusion 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.

AB - Background 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).Methods Three clinic-based studies were included: a casecohort study of 217 breast cancer case subjects and 2094 non-case subjects and two casecontrol 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 casecontrol 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.Results The variation measure was statistically significantly associated with the risk of breast cancer in all three studies (highest vs lowest quartile: HR = 7.0 [95% CI = 4.6 to 10.4]; OR = 10.7 [95% CI = 7.5 to 15.3]; OR = 2.6 [95% CI = 1.6 to 4.2]; all Ptrend <. 001). In two studies, the risk estimates and AUCs for the variation measure were greater than those for percent density (AUCs for variation = 0.71 and 0.76; AUCs for percent density = 0.65 and 0.65), whereas in the third study, these estimates were similar (AUC for variation = 0.60 and AUC for percent density = 0.61). A meta-analysis of the three studies demonstrated a stronger association between variation and breast cancer (highest vs lowest quartile: RR = 3.6, 95% CI = 1.9 to 7.0) than between percent density and breast cancer (highest vs lowest quartile: RR = 2.3, 95% CI = 1.9 to 2.9).Conclusion 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.

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