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J Natl Cancer Inst. 2012 Jul 3;104(13):1028-37. doi: 10.1093/jnci/djs254. Epub 2012 Jul 3.

A novel automated mammographic density measure and breast cancer risk.

Author information

1
Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, 200 First St. SW, Rochester, MN 55905, USA.

Erratum in

  • J Natl Cancer Inst. 2012 Nov 7;104(21):1687-90.

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 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.

RESULTS:

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).

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.

PMID:
22761274
PMCID:
PMC3634551
DOI:
10.1093/jnci/djs254
[Indexed for MEDLINE]
Free PMC Article

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