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Portrait of Predrag Bakic. Photo

Predrag Bakic

Associate Professor

Portrait of Predrag Bakic. Photo

Parenchymal Texture Analysis in Digital Breast Tomosynthesis for Breast Cancer Risk Estimation. A Preliminary Study

Author

  • Despina Kontos
  • Predrag R. Bakic
  • Ann Katherine Carton
  • Andrea B. Troxel
  • Emily F. Conant
  • Andrew D.A. Maidment

Summary, in English

Rationale and Objectives: Studies have demonstrated a relationship between mammographic parenchymal texture and breast cancer risk. Although promising, texture analysis in mammograms is limited by tissue superposition. Digital breast tomosynthesis (DBT) is a novel tomographic x-ray breast imaging modality that alleviates the effect of tissue superposition, offering superior parenchymal texture visualization compared to mammography. The aim of this study was to investigate the potential advantages of DBT parenchymal texture analysis for breast cancer risk estimation. Materials and Methods: DBT and digital mammographic (DM) images of 39 women were analyzed. Texture features, shown in previous studies with mammograms to correlate with cancer risk, were computed from the retroareolar breast region. The relative performances of the DBT and DM texture features were compared in correlating with two measures of breast cancer risk: (1) the Gail and Claus risk estimates and (2) mammographic breast density. Linear regression was performed to model the association between texture features and increasing levels of risk. Results: No significant correlation was detected between parenchymal texture and the Gail and Claus risk estimates. Significant correlations were observed between texture features and breast density. Overall, the DBT texture features demonstrated stronger correlations with breast percent density than DM features (P ≤ .05). When dividing the study population into groups of increasing breast percent density, the DBT texture features appeared to be more discriminative, having regression lines with overall lower P values, steeper slopes, and higher R2 estimates. Conclusion: Although preliminary, the results of this study suggest that DBT parenchymal texture analysis could provide more accurate characterization of breast density patterns, which could ultimately improve breast cancer risk estimation.

Publishing year

2009-03

Language

English

Pages

283-298

Publication/Series

Academic Radiology

Volume

16

Issue

3

Document type

Journal article

Publisher

Elsevier

Topic

  • Medical Engineering
  • Cancer and Oncology

Keywords

  • breast cancer risk estimation
  • Digital breast tomosynthesis
  • digital mammography
  • parenchymal texture analysis

Status

Published

ISBN/ISSN/Other

  • ISSN: 1076-6332