Abstract #1474

Predictive Model for Breast Lesion Assessment Using Restriction Spectrum Imaging

Stephane Loubrie¹, Ana Rodriguez-Soto¹, Tyler Seibert^1,2,3, Michael Hahn¹, Vandana Dialani^4,5, Catherine Wei⁶, Zahra Karimi⁴, Joshua Kuperman¹, Anders Dale^1,7, Etta Pisano^4,8, Jingjing Zou⁹, and Rebecca Rakow-Penner^1,3

¹Radiology, University of California, San Diego, San Diego, CA, United States, ²Radiation medicine, University of California, San Diego, San Diego, CA, United States, ³Bioengineering, University of California, San Diego, San Diego, CA, United States, ⁴Radiology, Beth Israel Hospital, Boston, MA, United States, ⁵Harvard Medical School, Boston, MA, United States, ⁶Commonwealth Radiology Associates, Boston, MA, United States, ⁷Neurosciences, University of California, San Diego, San Diego, CA, United States, ⁸American College of Radiology, Reston, VA, United States, ⁹Biostatistics, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, San Diego, CA, United States

Synopsis

Keywords: Breast, Diffusion/other diffusion imaging techniquesDiffusion weighted imaging (DWI) holds great potential in improving specificity of findings detected on contrast enhanced breast MRI. Restriction spectrum imaging (RSI), an advanced diffusion imaging model, has potential in discriminating between malignant and fibroglandular breast tissue. In this abstract, we evaluate RSI’s performance, combined with a random forest model, in differentiating lesions requiring biopsies from lesions that do not. The model showed significant preliminary results.

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