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Proceedings Paper

Association of a mammographic parenchymal pattern (MPP) descriptor with breast cancer risk: a case-control study
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Paper Abstract

We are investigating the feasibility of improving breast cancer risk prediction by computerized mammographic parenchymal pattern (MPP) analysis. A case-control study was conducted to investigate the association of the MPP measures with breast cancer risk. The case group included 168 contralateral CC-view mammograms of breast cancer patients dated at least one year prior to cancer diagnosis, and the control group included 522 CC-view mammograms from one breast of normal subjects. We extracted and compared four types of statistical texture feature spaces that included run length statistics and region size statistics (RLS/RSS) features, spatial gray level dependence (SGLD) features, gray level difference statistics (GLDS) features, and the feature space combining these three types of texture features. A linear discriminant analysis (LDA) classifier with stepwise feature selection was trained and tested with leave-one-case-out resampling to evaluate whether the breast parenchyma of future cancer patients could be distinguished from those of normal subjects in each feature space. The areas under ROC curves (Az) were 0.71, 0.72, 0.71 and 0.76 for the four feature spaces, respectively. The Az obtained from the combined feature space was significantly (p<0.05) higher than those from the individual feature spaces. Odd ratios (OR) were used to assess the association between breast cancer risk and four categories of MPP measures: <0.1 (C1), 0.1-0.15 (C2), 0.15-0.2 (C3), and >0.2 (C4) while patient age was treated as a confounding factor. The adjusted ORs of breast cancer for C2, C3 and C4 were 3.23, 7.77 and 25.43, respectively. The preliminary result indicated that our proposed computerized MPP measures were strongly associated with breast cancer risk.

Paper Details

Date Published: 9 March 2010
PDF: 8 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76240A (9 March 2010); doi: 10.1117/12.844040
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)
Mark A. Helvie, Univ. of Michigan (United States)
Lubomir M. Hadjiiski, Univ. of Michigan (United States)
Berkman Sahiner, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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