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Abstract #3363

A 3D Shape and Textural Classification Tool for Identifying Malignant Breast Cancer

Rebecca E. Thornhill1, Greg O. Cron2, Kevin Ibach1, Shilpa Lad1, Mark E. Schweitzer1, Jean Seely1

1Medical Imaging, The Ottawa Hospital, Ottawa, Ontario, Canada; 2Medical Imaging, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada

While breast MRI has shown tremendous promise for characterizing breast cancers, its specificity has been limited by reliance on tumor shape. Many tumors will exhibit discrete areas of high perfusion or vascular leakiness. These hot spots could yield important information that would be obscured by reporting the average tumor Ktrans. In this study, we have identified a potential recipe for predicting malignant breast cancer comprising of shape and textural features. While textural features appear to provide good specificity and modest sensitivity, the converse was true for shape-based models. With further optimization, this approach may improve accuracy compared to conventional MRI.