Cancer

 Developing a Novel 3D Imaging Tool for Intraoperative Specimen Assessment during   Breast Cancer Resection: A Pilot Clinical Evaluation Study                  

  PI: Xiaoqin (Jennifer) Wang, MD; Co-I’s: Gibbs, Richard, MD; Qiong Han, MD;                      Yu Zhang, PhD; Nathan Jacobs, PhD; and Jinze Liu, PhD                                                                                                                                                                                                                   Xiaoqin (Jennifer) Wang, MD, Assistant Professor in Radiology with a                         subspeciality in breast imaging, is Principal Investigator of this retrospective                       3D imaging breast cancer resection research. The study is funded by an                              American Cancer Society grant. Presently mammogram is the standard screening            tool for the general population and has been proven to reduce mortality inclinical              trials. Mammography, however, is not perfect, diagnosis can be missed, especially in        patients with dense   breast. Dr. Wang proposes to develop a deep learning                     tool, using imaging which will   help radiologists detect breast cancer more                         accurately, improving radiologist’s efficiency, improvement in clinical operations, but       more importantly improved patient care benefiting more women. Application of this new tool, known as artificial intelligence (A1) in breast cancer screening will transform the screening challenges, moving clinical practice toward reduced call back rates. Lower call backs can eliminate unnecessary medical costs for redundant biopsies, decrease patient’s psychological stress, and ultimately may decrease the risk of developing invasive breast cancer in millions of women. Women who are needlessly called back yearly leading to neglected mammography screening. Dr. Wang’s research team/collaborators are Gibbs, Richard, MD; Qiong Han, MD; Yu Zhang, PhD.; Nathan Jacobs, PhD; and Jinze Liu, PhD. Research is ongoing; to date she and her research team have reviewed over 2,000 patient cases, to which interim findings are published in the following abstract: “Classification of Whole Mammogram and Tomosynthesis Images Using Deep Convolutional Neural Networks”, IEEE Trans Nanobioscience 2018 Jul,17(3):237-242.doi: 10.1109/TNB 22018.2845103. Epub 2018 June 7,   https://www.ncbi.nlm.nih.gov/pubmed/29994219

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