In Silico Tools for Investigating the Performance of Breast Cancer Imaging Technologies
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Breast cancer screening programs using two-dimensional (2D) digital mammography (DM), have proven effective in early detection of cancer, subsequently reducing breast cancer-related deaths. A major drawback of DM arises from large amounts of overlapping breast tissues which may mimic or conceal abnormalities in a 2D image. Advanced breast imaging technologies like digital breast tomosynthesis (DBT) generating 3D information are now being considered as a replacement for DM in screening programs. However, the benefits of DBT-based screening for earlier detection of cancer, across the various commercially available detector technologies are yet to be established. The aim of this thesis is to investigate the influence of x-ray imager technologies and imaging modalities on the early detection of breast cancer using in silico trials. The first part of this thesis focuses on developing computational models that replicate the growth of cancerous lesions and the physics of commercially available DM/DBT systems. I propose a growth model for breast lesions based on biological and physiological phenomena accounting for the stiffness of surrounding anatomical structures. Depending on the breast local anatomical structures, a range of unique lesion morphology was realized. Imaging physics models were developed to simulate direct and indirect x-ray detector technology. Image quality metrics were compared against measured data from three commercially available DM/DBT systems. Finally, these tools combined with the VICTRE 1.0 in silico framework were used to design in silico trials to study whether DBT can facilitate the detection of breast cancer at earlier disease stages and for a range of detector technologies. The in silico studies suggest that while DBT shows clear advantages for detecting masses at earlier stages, its benefits over DM for detecting micro-calcifications depend on the detector technology.
Co-Chairs: Dr. Aldo Badano and Dr. Heath Hofmann