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3 March 2009Comparison of breast parenchymal pattern on prior mammograms of breast cancer patients and normal subjects
We are investigating the feasibility of predicting the risk of developing breast cancer in future years by analysis of breast
parenchymal patterns on mammograms. A data set of CC-view mammograms from prior exams of 96 cancer patients
and 491 normal subjects was collected from patient files. The prior mammograms were obtained at least one year
before diagnosis for cancer patients and two-years of cancer-free follow-up for normal subjects. The percent dense
area was estimated by automated gray-level histogram analysis. Texture features were extracted from a region of
interest in the retroareolar area. A feature space was constructed by using the percent dense area and texture features in
combination with patient age. A linear discriminant analysis (LDA) classifier with stepwise feature selection was
trained to evaluate whether the breast parenchyma of future cancer patients can be distinguished from those of normal
subjects in the selected feature space. The areas under receiver operating characteristic curves (Az) were 0.90±0.02,
0.86±0.02, and 0.69±0.02 for the classification of future cancer patients and normal subjects by using the breast that
would develop cancer (M-vs-N), the contralateral breast (CoM-vs-N) and the patient age only (C-vs-N-with-Age),
respectively. The difference in the Az values between the M-vs-N and CoM-vs-N approaches did not achieve statistical
significance (p=0.11) by using ROC analysis. The performances of M-vs-N and CoM-vs-N were significantly better
than that of C-vs-N-with-Age (p<0.05). Our preliminary result indicates that computerized mammographic
parenchymal analysis might be useful for predicting the elevated risk of developing breast cancer in future years.
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Jun Wei, Heang-Ping Chan, Chuan Zhou, Yi-ta Wu, Berkman Sahiner, Lubomir M. Hadjiiski, Mark A. Helvie M.D., "Comparison of breast parenchymal pattern on prior mammograms of breast cancer patients and normal subjects," Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72602H (3 March 2009); https://doi.org/10.1117/12.813565