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. 2009 Mar;18(3):837-45.
doi: 10.1158/1055-9965.EPI-08-0631. Epub 2009 Mar 3.

Texture features from mammographic images and risk of breast cancer

Armando Manduca  1 , Michael J Carston, John J Heine, Christopher G Scott, V Shane Pankratz, Kathy R Brandt, Thomas A Sellers, Celine M Vachon, James R Cerhan
Affiliations

Texture features from mammographic images and risk of breast cancer

Armando Manduca et al. Cancer Epidemiol Biomarkers Prev. 2009 Mar.

Abstract

Mammographic percent density (PD) is a strong risk factor for breast cancer, but there has been relatively little systematic evaluation of other features in mammographic images that might additionally predict breast cancer risk. We evaluated the association of a large number of image texture features with risk of breast cancer using a clinic-based case-control study of digitized film mammograms, all with screening mammograms before breast cancer diagnosis. The sample was split into training (123 cases and 258 controls) and validation (123 cases and 264 controls) data sets. Age-adjusted and body mass index (BMI)-adjusted odds ratios (OR) per SD change in the feature, 95% confidence intervals, and the area under the receiver operator characteristic curve (AUC) were obtained using logistic regression. A bootstrap approach was used to identify the strongest features in the training data set, and results for features that validated in the second half of the sample were reported using the full data set. The mean age at mammography was 64.0+/-10.2 years, and the mean time from mammography to breast cancer was 3.7+/-1.0 (range, 2.0-5.9 years). PD was associated with breast cancer risk (OR, 1.49; 95% confidence interval, 1.25-1.78). The strongest features that validated from each of several classes (Markovian, run length, Laws, wavelet, and Fourier) showed similar ORs as PD and predicted breast cancer at a similar magnitude (AUC=0.58-0.60) as PD (AUC=0.58). All of these features were automatically calculated (unlike PD) and measure texture at a coarse scale. These features were moderately correlated with PD (r=0.39-0.76), and after adjustment for PD, each of the features attenuated only slightly and retained statistical significance. However, simultaneous inclusion of these features in a model with PD did not significantly improve the ability to predict breast cancer.

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Conflict of interest statement

Disclosure of Potential Conflicts of Interest: No potential conflicts of interest were disclosed.

Figures

Figure 1
Figure 1
Area under the ROC curve by decreasing scale for Laws features.
Figure 2
Figure 2
Area under the ROC curve by decreasing octave level for wavelet features.
Figure 3
Figure 3
Area under the ROC curve by increasing frequency bin for Fourier features.
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