Abstract
Purpose: The purpose of the study was to evaluate the usefulness of power law spectral analysis on mammographic parenchymal patterns in breast cancer risk assessment. Materials and Methods: Mammograms from 172 subjects (30 women with the BRCA1/BRCA2 gene mutation and 142 low-risk women) were retrospectively collected and digitized. Because age is a very important risk factor, 60 low-risk women were randomly selected from the 142 low-risk subjects and were age matched to the 30 gene mutation carriers. Regions of interest were manually selected from the central breast region behind the nipple of these digitized mammograms and subsequently used in power spectral analysis. The power law spectrum of the form \(P\left( f \right) = {B \mathord{\left/ {\vphantom {B {f^\beta }}} \right. \kern-\nulldelimiterspace} {f^\beta }}\) was evaluated for the mammographic patterns. The performance of exponent β as a decision variable for differentiating between gene mutation carriers and low-risk women was assessed using receiver operating characteristic analysis for both the entire database and the age-matched subset. Results: Power spectral analysis of mammograms demonstrated a statistically significant difference between the 30 BRCA1/BRCA2 gene mutation carriers and the 142 low risk women with an average β values of 2.92 (±0.28) and 2.47(±0.20), respectively. An A z value of 0.90 was achieved in distinguishing between gene mutation carriers and low-risk women in the entire database, with an A z value of 0.89 being achieved on the age-matched subset. Conclusions: The BRCA1/BRCA2 gene mutation carriers and low-risk women have different mammographic parenchymal patterns. It is expected that women identified as high risk by computerized feature analyses might potentially be more aggressively screened for breast cancer.
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Acknowledgments
We would like to thank Zhimin Huo, Ph.D., for initial studies on the database, Arthur E. Burgess, Ph.D., for useful discussions, Barbara L. Weber, M.D., for contributing cases to the database, and Dulcy E. Wolverton, M.D., for reviewing the mammograms. This work was supported in parts by USPHS grants R01-CA89452, R21-CA113800, and P50-CA125183 and by a grant from the US Army Medical Research and Materiel Command (DAMD 98-1209). M. L. Giger is a shareholder in R2/Hologic (Sunnyvale, CA). It is the policy of the University of Chicago that investigators disclose publicly actual or potential significant financial interests that may appear to be affected by the research activities.
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Li, H., Giger, M.L., Olopade, O.I. et al. Power Spectral Analysis of Mammographic Parenchymal Patterns for Breast Cancer Risk Assessment. J Digit Imaging 21, 145–152 (2008). https://doi.org/10.1007/s10278-007-9093-9
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DOI: https://doi.org/10.1007/s10278-007-9093-9