CHICAGO, Nov. 25, 2018 /PRNewswire/ -- The performance of QVCAD, the first AI CAD system FDA-Approved for concurrent reading of Automated Breast Ultrasound (ABUS) exams, will be covered in research presented at the 104th Annual Radiological Society of North America (RSNA) meeting, November 25-30, 2018 (South Hall #3572).

In the study, "Comparison of Automated Breast Ultrasound (ABUS) QVCAD Standalone Performance on Somo•v and Invenia Cases (BR253-SD-TUB7, Tuesday, November 27, 12:45-1:15 PM, BR Community, Learning Center, Station #7), researchers evaluated QVCAD standalone performance on somo•v and Invenia ABUS cases. The study was designed to build on a previous observer study had been performed with somo•v cases and to infer likely effect of QVCAD on Invenia cases. The study involved 164 somo•v (31 with cancer) and 145 Invenia (25 with cancer) cases, all of BI-RADS density C or D. Area under ROC curve (AUC), per-patient sensitivity and specificity, and false-positives per volume were compared. Results showed that somo•v and Invenia results were comparable and QVCAD standalone performance is comparable on somo•v and Invenia cases. The results suggest that the benefit of QVCAD for reducing reading time and producing non-inferior reader performance can be expected on Invenia cases.

With the recent emergence of AI and the FDA approval of QVCAD as a radiologist assist, Bob Wang, CEO of QView Medical offers his visionary insight into the future of Breast Screening. This whitepaper, "QView's Artificial Intelligence Technology the "QVCAD" and its Potential Impact on Breast Cancer Screening and a Clear Path to Saving Many More Lives," is available at the QView Medical RSNA press room.

QVCAD will also be featured in four sessions of the GE Vender Workshop. The session, "Introduction to QView and QVCAD/ABUS Case Review - The AI Deep Learning Radiology Assist in Reviewing GE Invenia ABUS Cases" will be held Sunday-Wednesday at 11am-12noon in Booth 8156. These sessions, led by Dr Kiyoshi Namba, Director of the Breast Cancer Center at Hokuto Healthcare Group in Japan, will cover the latest technological advancements in ABUS design and performance. Attendees will learn how improvements in workflow and image quality have the potential to increase cancer detection in women with dense breast tissue.

Being demonstrated at RSNA for the first time since being approved, QVCAD reduces interpretation time of screening ABUS exams while maintaining diagnostic accuracy. Based on deep learning algorithms, the AI system is designed to detect suspicious areas of breast tissue that have characteristics similar to breast lesions and highlight suspicious area to distinguish potentially malignant lesions from normal breast tissue. QVCAD is FDA Approved for use with ABUS systems and has received the CE mark for use with ABUS/ABVS systems.

To improve reader productivity, QVCAD provides synthetic 2D images of all six volumetric datasets in a standard ABUS exam to provide an immediately visual overview of the case. The C-thru images, which are minimum intensity projections (MinIP), summarize each 3D ABUS volume in a 2D image and bring attention to specific areas of interest by enhancement of radial spiculations and retraction patterns in coronal reconstructions, which are highly suggestive of breast cancer in ABUS. Users may select any CAD mark or area of interest on the C-thru image and the corresponding original ABUS images will be displayed, enabling users to efficiently review the entire ABUS case.


About QView Medical
QView Medical, incorporated in 2006, developed its deep learning AI-CAD for automated breast ultrasound systems (ABUS). The QVCAD system is FDA approved and can now be used with any currently installed GE Invenia system. For more information, visit www.qviewmedical.com

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