NCI mulls genetic risk of prostate, breast cancer

New research suggests the capability of a mammography-based deep learning model to identify women at high risk of breast cancer led to more than triple the cancer detection rate on breast MRI in comparison to traditional risk assessment tools.

In the third episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss the challenges of expanded breast cancer screening amid a backdrop of radiologist shortages and ever-increasing volume on radiology worklists.

A robotic guidance and placement system, the IGAR system can be utilized inside of a magnetic resonance imaging (MRI) bore.

In the second episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss recent studies published by the Journal of the American Medical Association (JAMA) that suggested moving to more of a risk-adapted model for mammography screening.

In a prospective study of over 55,000 women who had screening mammography, researchers found that double-reading by a radiologist and artificial intelligence (AI) was non-inferior to double-reading by two radiologists in detecting breast cancer.

The Microvascular Imaging Super Resolution Contrast-Enhanced Ultrasound application reportedly leverages micro-bubble contrast media to provide enhanced spatial resolution for imaging of malignant lesions.