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Abstract #2330

Discrimination of breast cancer from healthy breast tissues using a tri-exponential model

Maren Marie Sjaastad Andreassen1, Ana Elvira Rodriguez-Zoto2, Cristopher Charles Conlin2, Igor Vidic3, Grace Sora Ahn4, Neil Peter Jerome1,5, Agnes Østlie5, Tone Frost Bathen1,5, Pål Erik Goa3,5, Rebecca Rakow-Penner2, and Anders Martin Dale2,6
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 Physics, Norwegian University of Science and Technology, Trondheim, Norway, 4School of Medicine, University of California, San Diego, La Jolla, CA, United States, 5Department of Radiology and Nuclear Medicine, Norwegian University of Science and Technology, Trondheim, Norway, 6Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States

The purpose of this work was to develop a method to discriminate breast cancer from healthy breast tissues using signal contribution maps from multi-b diffusion MRI acquisitions. Signal contributions were estimated using a tri-exponential model, with the ADC values for three distinct compartments assumed fixed across voxels and subjects. A linear discriminant function was constructed using the estimated signal contributions from the two lowest ADC components. Average ROC AUC for discriminating cancer from healthy breast tissues was 0.99 (CI95% = 0.98-1.00), superior to that of independent signal contributions, maximum b-value volume and conventional DWI estimates (ADC and apparent diffusion kurtosis).

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