Abstract #0193

Automated breast tumor segmentation in DWI using multi-modality image registration: a feasibility study using multi-center data

Nu N Le¹, David C Newitt¹, Wen Li¹, Deep Hathi¹, Jiachao Liang¹, Lisa J Wilmes¹, Jessica E Gibbs¹, Natsuko Onishi¹, Bonnie N Joe¹, John Kornak², Patrick J Bolan³, Savannah C Partridge⁴, and Nola M Hylton¹

¹Radiology, University of California, San Francisco, San Francisco, CA, United States, ²Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA, United States, ³Radiology, University of Minnesota, Minneapolis, MN, United States, ⁴Radiology, University of Washington, Seattle, WA, United States

Synopsis

Keywords: Breast, CancerDiffusion-weighted imaging (DWI) in MRI plays an important role in diagnostic applications. Automated tumor segmentation is an important yet challenging step for quantitative breast imaging analysis. While methods have been developed for dynamic contrast enhanced (DCE) MRI, automatic segmentation in breast DWI-MRI is still underdeveloped. The purpose of this study is to develop methods to transfer functional tumor volume (FTV) analysis ROI from DCE-MRI image space to DWI-MRI image space using image registration and extract ADC statistics in regions corresponding to those of DCE segmentations for prediction of pathologic treatment response (pCR).

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