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Abstract #4312

Automated Apparent Diffusion Coefficient Calculation Using Multimodal Image Registration for Prediction of Breast Cancer Treatment Response

Nu N. Le1, Wen Li1, Lisa Wilmes1, Natsuko Onishi1, Jessica Gibbs1, Bonnie Joe1, John Kornak1, Dariya Malyarenko2, Thomas Chenevert2, Patrick Bolan3, Savannah Partridge4, and Nola Hylton1
1University of California, San Francisco, San Francisco, CA, United States, 2University of Michigan, Ann Arbor, MI, United States, 3University of Minnesota, Minneapolis, MN, United States, 4University of Washington, Seattle, WA, United States

Synopsis

Keywords: Breast, Breast, Cancer, Image Registration, Treatment Response

Motivation: Tumor delineation is a challenging but critical step for ADC calculation in Diffusion-weighted (DW) MRI. Automated delineation methods are still underdeveloped for DW-MRI.

Goal(s): To compare the predictive performance of manual vs. automated ADC values at multiple timepoints during neoadjuvant treatment.

Approach: We used MRI data from the ACRIN 6698 trial for this analysis. Automated ADC values were computed using transformed ROIs from image registration between pre-contrast DCE and DWI (b=0).

Results: Predictive performance improved with automated ADC values at 3-week timepoint and remained similar at 12-week and pre-surgery timepoints.

Impact: This work offers a practical approach for automated ADC calculation, allowing radiologists to expedite clinical decisions for breast cancer patients at early treatment timepoints; therefore, improving patient care.

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