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24 March 2016 Increasing cancer detection yield of breast MRI using a new CAD scheme of mammograms
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Although breast MRI is the most sensitive imaging modality to detect early breast cancer, its cancer detection yield in breast cancer screening is quite low (< 3 to 4% even for the small group of high-risk women) to date. The purpose of this preliminary study is to test the potential of developing and applying a new computer-aided detection (CAD) scheme of digital mammograms to identify women at high risk of harboring mammography-occult breast cancers, which can be detected by breast MRI. For this purpose, we retrospectively assembled a dataset involving 30 women who had both mammography and breast MRI screening examinations. All mammograms were interpreted as negative, while 5 cancers were detected using breast MRI. We developed a CAD scheme of mammograms, which include a new quantitative mammographic image feature analysis based risk model, to stratify women into two groups with high and low risk of harboring mammography-occult cancer. Among 30 women, 9 were classified into the high risk group by CAD scheme, which included all 5 women who had cancer detected by breast MRI. All 21 low risk women remained negative on the breast MRI examinations. The cancer detection yield of breast MRI applying to this dataset substantially increased from 16.7% (5/30) to 55.6% (5/9), while eliminating 84% (21/25) unnecessary breast MRI screenings. The study demonstrated the potential of applying a new CAD scheme to significantly increase cancer detection yield of breast MRI, while simultaneously reducing the number of negative MRIs in breast cancer screening.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Maxine Tan, Faranak Aghaei, Alan B. Hollingsworth, Rebecca G. Stough, Hong Liu, and Bin Zheng "Increasing cancer detection yield of breast MRI using a new CAD scheme of mammograms", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97850R (24 March 2016);

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