Cancer

Novel 3D Imaging During Breast Cancer Resection

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

PI: Xiaoqin (Jennifr) Wang, MD; Co-I's: Richard Gibbs, MD; Han Qiong, 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 in clinical 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 (AI) in breast cancer screening will transform the screening challenges, moving clinical practice toward reduced call back rates. Lower call backs can elimiate 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. Research is ongoing; to date she and her research team have reviewed over 2,000 patients 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/TNB22018.2845103. Epub 2018 June 7, http://www,ncbi.nlm.nih.gov/pubmed/29994219 

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