Abstract PS1-06-15: Low-Dose X-Ray Diffraction Imaging with Real-Time AI Classification Distinguishes DCIS from Invasive Breast Cancer: Hardware and Interim Clinical Performance from a First-in-Human Study

Abstract Low Dose X-Ray Diffraction Imaging with Real Time AI Classification Distinguishes DCIS from Invasive Breast Cancer: Hardware and Interim Clinical Performance of the EosDx EoScan™ First-in-Human StudyAika Tanaka1, Audrey Nguyen1, Mark Pegram2, Masroor Khonkhodzhaev3,4, Slava Shcherbakov4, DarylHoffman5, Alexander Lazarev1, Byron Aram1, Thomas J. Lomis6, Prashant Chawla6, Lev Mourokh3,Pavel Lazarev11 EosDx, Inc., Menlo Park, CA, USA 2 Stanford Cancer Institute, Stanford University CA, USA 3 PhysicsDepartment, Queens College of the City University of New York, NY, USA, 4 Matur UK Ltd., London,UK, 5 Daryl Hoffman MD, Campbell, CA USA, 6 San Fernando Valley Cancer Foundation, Van Nuys, CA,USA Background Xray diffraction detects nanoscale structural alterations in collagen, lipid, and water that heraldbreast neoplasia. EoScan™ is a diffraction breast scanner that captures Bragg signatures throughintact tissue and classifies malignancy using a convolutional neural network (CNN) trained on>12,700 spectra. First-in-Human Hardware The IRB-approved first-in-human unit ("Human-1", IRB #1381803, San Fernando Valley CancerCenter, Los Angeles, CA) comprises: (1) a micro-focus Xray source with liquid cooling, (2) a80 µm slot collimator, (3) a 256×256-pixel photon-counting detector housed in a 2 mmPb-equivalent shield. The system fits in