Abstract #1167

Technical Considerations for Implementing Multi-Center and Multi-Platform Quantitative DCE-MRI to Predict Breast Cancer Therapy Response

Brendan Moloney¹, Xin Li¹, Michael Hirano², Assim Saad Eddin³, Jeong Youn Lim¹, Debosmita Biswas², Anum S. Kazerouni², Alina Tudorica¹, Isabella Li², Mary Lynn Bryant², Courtney Wille³, Chelsea Pyle¹, Habib Rahbar², Su Kim Hsieh³, Travis Rice-Stitt¹, Suzanne Dintzis², Amani Bashir³, Evthokia Hobbs¹, Alexandra Zimmer¹, Jennifer Specht², Sneha Phadke³, Nicole Fleege³, James H. Holmes³, Savannah C. Partridge², and Wei Huang¹

¹Oregon Health & Science University, Portland, OR, United States, ²University of Washington, Seattle, WA, United States, ³University of Iowa, Iowa City, IA, United States

Synopsis

Keywords: Treatment Response, Quantitative Imaging, Multi-Center and Multi-Vendor Platform, DCE-MRI, Breast Cancer, Therapy Response, Ktrans

Motivation: Determine best-practice quantitative DCE-MRI for predicting breast cancer (BC) response to neoadjuvant chemotherapy (NAC) in a multi-center (MC) and multi-vendor platform (MP) setting.

Goal(s): Evaluate effects of different pharmacokinetic analysis approaches on K^trans and its predictive performance.

Approach: 15 BC patients treated with NAC underwent longitudinal DCE-MRI at 3 sites using 3T systems from 3 vendors. Variations in analysis included Tofts model vs. Shutter-Speed model (SSM), ROI- vs. voxel-based analysis, and using fixed vs. measured R₁₀.

Results: Different analysis approaches resulted in significantly different K^trans, with SSM K^trans from voxel-based analysis using fixed R₁₀ showing highest predictive accuracy for response.

Impact: Voxel-based SSM analysis using fixed R₁₀ takes advantage of greater range of SSM K^trans changes in response to therapy, mitigates R₁₀ measurement errors, and may be the best-practice quantitative DCE-MRI for predicting NAC response in a MC and MP setting.

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