Association of Computerized Mammographic Parenchymal Pattern Measure with Breast Cancer Risk: A Pilot Case-Control Study

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The proposed mammographic parenchymal pattern measure showed a strong association with breast cancer risk and a low correlation with percentage density and some known risk factors; it may serve as an independent factor in a breast cancer prediction model.


To develop a computerized mammographic parenchymal pattern (MPP) measure and investigate its association with breast cancer risk.

Materials and Methods

A pilot case-control study was conducted by collecting mammograms from 382 subjects retrospectively. The study was institutional review board approved and HIPAA compliant. Informed consent was waived. The cases included the contralateral mammograms of cancer patients (n = 136) obtained at least 1 year before diagnosis. The controls included mammograms of healthy subjects (n = 246) who had cancer-free follow-up for at least 5 years. The data set was historically divided into a training set and an independent test set. An MPP measure was designed to analyze the texture patterns of fibroglandular tissue in the retroareolar region. Odds ratios (ORs) were used to assess the association between breast cancer risk and MPP. To test the trend in ORs, we divided the MPP measure into three categories (C1, C2, and C3) on the basis of its values from low to high, with C1 as the baseline. The confounding factors in this study included patient age, body mass index, first-degree relatives with history of breast cancer, number of previous breast biopsies, and percentage density (PD).


Among all of the subjects from the training and test data sets, the Pearson product-moment correlation coefficient between MPP and PD was 0.13. With logistic regression to adjust the confounding, the adjusted ORs for C2 and C3 relative to C1 in the test set were 2.82 (P = .041) and 13.89 (P < .001), respectively.


The proposed MPP measure demonstrated a strong association with breast cancer risk and has the potential to serve as an independent factor for risk prediction.

© RSNA, 2011

Supplemental material:


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Article History

Received June 28, 2010; revision requested August 16; final revision received December 2; accepted January 13, 2011; final version accepted January 20.
Published online: July 2011
Published in print: July 2011