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Proceedings Paper

Comparison of breast parenchymal pattern on prior mammograms of breast cancer patients and normal subjects
Author(s): Jun Wei; Heang-Ping Chan; Chuan Zhou; Yi-ta Wu; Berkman Sahiner; Lubomir M. Hadjiiski; Mark A. Helvie M.D.
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Paper Abstract

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.

Paper Details

Date Published: 3 March 2009
PDF: 6 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72602H (3 March 2009); doi: 10.1117/12.813565
Show Author Affiliations
Jun Wei, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Chuan Zhou, Univ. of Michigan (United States)
Yi-ta Wu, Univ. of Michigan (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Lubomir M. Hadjiiski, Univ. of Michigan (United States)
Mark A. Helvie M.D., Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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