Breast Imaging

A Kaiser score > 5 with breast MRI demonstrated a 97 percent sensitivity and 87.9 percent specificity for predicting malignancy in patients with BI-RADS 0 or 4 presentations on mammography and breast ultrasound, according to a new study.

The use of adjunctive CT bone reading software in women with breast cancer detected between 8 to 22 percent more metastatic lesions for reviewing radiologists and reduced CT interpretation time by 50 percent or more, according to a new study.

In multivariable external testing, tumor structure habitat phenotypes from pre-treatment MRI revealed more than a doubling of recurrence risk for women with stage 2 and stage 3 breast cancer.

New research suggests that photon-counting CT provides comparable sensitivity to breast MRI for lesion characterization, higher accuracy than MRI for nodal metastasis and lower detection of microcalcifications in comparison to full-field digital mammography.

This summer's breast imaging research spanned digital breast tomosynthesis (DBT) efficiency gains, AI models that flag cancer risk years before diagnosis, a cautionary study on automation bias, and advances in ultrasound, MRI, and contrast-enhanced mammography (CEM). Here is a look back at the most well-viewed breast imaging content of the summer.

In comparison to standard readout-segmented echo-planar imaging, the combination of deep learning reconstruction and multi-shot diffusion-weighted imaging (DWI) offered significantly higher SNR, CNR and lesion contrast with a greater than a two-minute reduction in scan time.

Catch up on the latest episodes of our Breast Imaging in Focus series with Manisha Bahl, MD, MPH, FSBI, discussing research on a variety of topics including breast arterial calcification on mammograms, automated breast ultrasound, AI and GLP-1 receptor agonists.

General radiologist assessment of mammograms with adjunctive AI led to a significant increase in cancer detection rate akin to that of breast imaging specialists, according to new research.

In cases involving false negative suggestions from adjunctive AI for screening mammogram review, researchers found that unassisted radiologists had a 32 percent higher sensitivity rate than those who utilized AI.

In a recent interview, Amy Patel, MD, discussed recent research on breast cancer screening, pertinent obstacles and keys to facilitating improved adherence with mammography screening.