Correction

An emerging deep learning radiomics model, based on B-mode ultrasound and color Doppler flow imaging, demonstrated a 91 percent AUC for predicting lymphovascular invasion in a multicenter study of women with invasive breast cancer.

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.

One of the recommendations from the European Society of Breast Imaging (EUSOBI) is annual breast MRI exams starting at 25 years of age for women deemed to be at high risk for breast cancer.

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 comparison to energy-integrating detector CT for the workup of suspected acute pulmonary embolism, the use of photon-counting detector CT reduced radiation dosing by 48 percent, according to newly published research.

The artificial intelligence (AI)-powered applications reportedly allow clinicians to diagnose pulmonary edema and measure left ventricle ejection fraction within seconds.