Computerized prediction of risk for developing breast cancer based on bilateral mammographic breast tissue asymmetry

Xingwei Wang, Dror Lederman, Jun Tan, Xiao Hui Wang, Bin Zheng

Research output: Contribution to journalArticle

57 Scopus citations

Abstract

This study developed and assessed a computerized scheme to detect breast abnormalities and predict the risk of developing cancer based on bilateral mammographic tissue asymmetry. A digital mammography database of 100 randomly selected negative cases and 100 positive cases for having high-risk of developing breast cancer was established. Each case includes four images of cranio-caudal (CC) and medio-lateral oblique (MLO) views of the left and right breast. To detect bilateral mammographic tissue asymmetry, a pool of 20 computed features was assembled. A genetic algorithm was applied to select optimal features and build an artificial neural network based classifier to predict the likelihood of a test case being positive. The leave-one-case-out validation method was used to evaluate the classifier performance. Several approaches were investigated to improve the classification performance including extracting asymmetrical tissue features from either selected regions of interests or the entire segmented breast area depicted on bilateral images in one view, and the fusion of classification results from two views. The results showed that (1) using the features computed from the entire breast area, the classifier yielded the higher performance than using ROIs, and (2) using a weighted average fusion method, the classifier achieved the highest performance with the area under ROC curve of 0.781 ± 0.023. At 90% specificity, the scheme detected 58.3% of high-risk cases in which cancers developed and verified 6-18 months later. The study demonstrated the feasibility of applying a computerized scheme to detect cases with high risk of developing breast cancer based on computer-detected bilateral mammographic tissue asymmetry.

Original languageEnglish (US)
Pages (from-to)934-942
Number of pages9
JournalMedical Engineering and Physics
Volume33
Issue number8
DOIs
StatePublished - Oct 1 2011

Keywords

  • Breast cancer
  • Breast tissue asymmetry
  • Computer-aided detection
  • Mammography
  • Risk assessment

ASJC Scopus subject areas

  • Biophysics
  • Biomedical Engineering

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