Abstract #1434

Three-component diffusion model with deformable registration for automated evaluation of response to neoadjuvant therapy in breast cancer

Maren M. Sjaastad Andreassen¹, Michelle W. Tong², Ana E. Rodríguez-Soto², Somaye Zare³, Tyler M. Seibert^2,4,5, Michael Hahn², Haydee Ojeda-Fournier ², Neil P. Jerome^1,6, Tone F. Bathen^1,7, Anders M. Dale^2,8, and Rebecca Rakow-Penner²

¹Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway, ²Department of Radiology, University of California San Diego, La Jolla, CA, United States, ³Department of Pathology, University of California San Diego, La Jolla, CA, United States, ⁴Department of Bioengineering, University of California San Diego, La Jolla, CA, United States, ⁵Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, United States, ⁶Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway, ⁷Department of Radiology and Nuclear Medicine, Norwegian University of Science and Technology, Trondheim, Norway, ⁸Department of Neuroscience, University of California San Diego, La Jolla, CA, United States

The purpose of this work was to develop a method to automatically monitor response to neoadjuvant treatment in breast cancer using longitudinally registered multi-b diffusion MRI acquisitions. The two slowest diffusion signal contributions from a three-component model were used to generate cancer probability maps (C₁C₂ classifier) that estimated tumor volume at each timepoint. Our results demonstrated that changes in C₁C₂ classifier agreed with standard dynamic-contrast-enhanced MRI criteria (RECIST) in 23 out of 24 cases. In 35% of treatment responders, the C₁C₂ classifier captured response at an earlier timepoint than RECIST.

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