Using risk model-based strategies to determine who should undergo low-dose CT lung cancer screening is more cost effective than current U.S. Preventive Services Task Force guidelines.

Researchers involved in the new study propose that abbreviated MRI protocols could offer more accurate assessments of liver lesions than ultrasound. 

Age, specialty training and reading volume give readers an edge over their peers when it comes to screening mammogram interpretations.

The deep learning model was trained to predict risk of lung cancer in the one to six years following completion of an LDCT scan, and it does not require clinical information relative to risk factors to do so.

Gwendolyn Bryant-Smith, MD, division chief of breast imaging and associate director for diversity, equity and inclusion at the Winthrop P. Rockefeller Cancer Institute, University of Arkansas, discussed how her center addressed health inequities in mammography.

“This can have a major impact on the younger population, especially for women at high risk of developing breast cancer, like those with genetic mutations, where there is a necessity to start screenings at an early age,” experts explained in Academic Radiology.

Prior to 2018, it was recommended that women at higher-than-average risk undergo supplemental breast MRI screening only if they had a personal history of breast cancer in addition to carrying a hereditary breast cancer gene mutation.

These notifications can sometimes leave many patients feeling even more bewildered about their mammogram results than they were before being made aware of their dense tissue.