Sequence depicts noncontrast MRA

In a study involving assessment of over 1,000 thyroid nodules, researchers found the machine learning model led to substantial increases in sensitivity and specificity for estimating the risk of thyroid malignancy over traditional TI-RADS and guidelines from the American Thyroid Association.

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.

Does continuous learning fuel our drive, our dedication, and, perhaps, our dreams?

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.

For the assessment of contrast-enhanced abdominopelvic CT exams, an artificial intelligence model demonstrated equivalent or better sensitivity than radiologist readers, and greater than 90 percent specificity for the diagnosis of colorectal cancer.

18F-DCFPyL facilitated detection of recurrent prostate cancer in 51 percent of patients with PSA levels ranging between 0.2 to 0.5 ng/ml, according to new research presented at the American Society of Clinical Oncology Genitourinary Cancers (ASCO-GU) Symposium.