Using contrast-enhanced digital mammography (CEDM) to evaluate low to moderately suspicious breast lesions can greatly reduce biopsies in patients with benign lesions, according to a study published Sept. 5 in Academic Radiology.
Providing patients more testing choices does not increase their participation in colorectal cancer screening—but the way clinician’s framed those choices did, according to a new study published in JAMA Network Open.
Among patients who show any indication for radiotherapy, black women with breast cancer were more likely to receive radiation compared to white patients, according to a recent study published in Advances in Radiation Oncology.
Synthesized digital mammography (SM) was created to help reduce the radiation dose for patients undergoing digital mammography (DM) in digital breast tomosynthesis (DBT), so why haven’t more clinics adopted it?
A new machine learning system created by UCLA researchers may help doctors classify breast cancers that are notoriously difficult to diagnose, according to an Aug. 9 study published in JAMA Network Open.
Utilizing an AI system for digital breast tomosynthesis (DBT) can improve radiologists’ accuracy while dramatically reducing reading times, according to a new study published in Radiology: Artificial Intelligence.
A deep learning classification approach can identify cancerous regions from benign areas in optical coherence tomography (OCT) images of breast tissue, according to results of a July 17 study published in Academic Radiology.
After looking at more than 12,500 preventative office visits included in the National Ambulatory Medical Care Survey, researchers reported that the rate of screening breast ultrasound ordering by physicians has remained low.