Could a Deep Learning Model for Mammography Improve Prediction of DCIS and Invasive Breast Cancer?
Artificial intelligence (AI) assessment of mammography images may significantly enhance the prediction of invasive breast cancer and ductal carcinoma in situ (DCIS) in women with breast cancer, according to new research presented at the Society for Breast Imaging (SBI) conference.
Improving the Quality of Breast MRI Acquisition and Processing
Discussing findings from a new study presented at the Society for Breast Imaging (SBI) conference, Shahrzad Tavana, M.D., detailed the significant impact of training sessions for MRI technologists in improving breast positioning, optimal field of view and accuracy of sequence submissions to PACS for breast MRI exams.
Radiology Challenges with Breast Cancer Screening in Women with Breast Implants
In a recent video interview, Stephen Rose, M.D., reviewed a variety of factors that can impact interpretation of breast imaging for women with breast implants and discussed recent research showing a 22 percent reduction in cancer detection rate for this population in comparison to women without breast implants.
New Study Shows Viability of Adjunctive AI for Breast Ultrasound
Adjunctive use of an artificial intelligence (AI) software demonstrated nearly equivalent sensitivity and over 28 percent higher accuracy in comparison to radiologist assessment of breast ultrasound images for breast lesions, according to new research presented at the recent Society of Breast Imaging (SBI) conference.
Is Digital Breast Tomosynthesis Superior to Digital Mammography?
A closer look at the literature suggests the combination of digital breast tomosynthesis (DBT) and two-dimensional (2D) digital mammography (DM) has relatively equivalent cancer detection rates as using DM alone, according to a recent presentation at the Society of Breast Imaging (SBI)/American College of Radiology (ACR) conference.