Dr. Etta Pisano, principal investigator for the American College of Radiology Imaging Network's (ACRIN) Digital Mammographic Imaging Screening Trial (DMIST), has been elected to the Institute of Medicine (IOM).
Pisano is vice dean for academic affairs at the University of North Carolina School of Medicine at Chapel Hill.
The DMIST study found that digital mammography detected significantly more cancers than film-screen mammography in women 50 and younger, as well as in premenopausal and perimenopausal women and women with dense breasts. Since study results were reported in 2005 in the New England Journal of Medicine, the number of imaging facilities with digital mammography equipment has increased more than 400%, according to the American College of Radiology.
Related Reading
DMIST: Women under 50 with dense breasts benefit from FFDM, January 29, 2008
RSNA studies delve deeper into DMIST results, December 14, 2006
More DMIST analysis supports FFDM in younger women, dense breasts, November 26, 2006
Do DMIST results underestimate FFDM's impact? October 24, 2005
DMIST study: Younger women may benefit most from digital mammo, September 16, 2005
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![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)






