Diagnostic Imaging's Weekly Scan: May 4 — May 10

Emerging research suggests that radiomic breast texture features extracted from full-field digital mammography may bolster prognostic assessments of invasive breast cancer risk beyond traditional risk factors.

In a second part of a new podcast episode on recently published research on projected radiation-induced cancers from computed tomography (CT) scans, Mahadevappa Mahesh, MS, Ph.D., and Joseph Cavallo, M.D., offer current perspectives on cardiac CT dosing, AI advances and the importance of teamwork in ensuring appropriate dosing for CT.

The use of AI software for digital breast tomosynthesis (DBT) facilitated a higher cancer detection rate and over double the PPV for exams with abnormal interpretation, according to a newly published study.

In a new podcast, Mahadevappa Mahesh, MS, Ph.D., and Joseph Cavallo, M.D., share their perspectives on recently published research looking at projections for future radiation-induced cancers from computed tomography (CT) scans.

New research findings suggest that image quality and lesion conspicuity are significantly lower with ultrafast breast MRI in comparison to standard dynamic contrast-enhanced breast MRI.

While AI-enabled hippocampal segmentation on MRI offered a 95 percent AUC for differentiating between Alzheimer’s disease and normal controls in a 27-study meta-analysis, it offered significantly lower sensitivity and specificity in differentiating mild cognitive impairment.