Dear Women's Imaging Insider,
There has been much ambiguity about how to describe background parenchymal enhancement in breast imaging reports. An update to the BI-RADS MRI lexicon, due out in November, should clear up any confusion.
During this month's International Symposium on State-of-the-Art Imaging (iSi) held in France, Dr. Debra Ikeda said she expects "a steep learning curve" for the new terms because inexperienced doctors don't know what to do with normal enhancing tissue. Read more by clicking here.
In addition to Dr. Ikeda's thoughts on the matter, there's much more to read about in our Women's Imaging Digital Community:
- Spanish researchers have reported on the effectiveness of five-day brachytherapy for endometrial cancer.
- A study of 17 countries shows that mammography quality varies but may easily be improved.
- British researchers have found that patients with breast cancer with higher levels of vascularization after two cycles of neoadjuvant chemotherapy are likely to have lower survival rates.
- FDG-PET/CT can provide information on the possible malignancy of ovarian tumors and help gynecologists determine the correct course of treatment for patients.
Not to mention, you'll also find information on dedicated breast CT and more.
After taking a look at our community, let me know what you think. What topics are you interested in? What would you like to see more of? Send me an email and let me know.
![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.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)
![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.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
