Fewer Recalls with Synthesized Mammography Plus Digital Breast Tomosynthesis
The use of synthesized mammography instead of additional full-field digital mammography has no significant effect on biopsy rate.
Synthesized mammography (SM) with digital breast tomosynthesis (DBT) lowers recall rate, according to a study published in the journal Academic Radiology.
Researchers from Johns Hopkins University in Baltimore, Md., and the University of Wisconsin in Madison sought to evaluate the screening performance of DBT combined with SM versus combined with full-field digital mammography (FFDM).
The researchers retrospectively reviewed 7,745 screening studies utilizing FFDM plus DBT and 14,776 with SM plus DBT. They compared recall rate, biopsy rate, positive predictive value 1 (PPV1), positive predictive value 3 (PPV3), and cancer detection rate (CDR) between the two groups.
The results showed the overall recall rate was 7.06 percent in the SM + DBT cohort, which was significantly lower compared to 7.63 percent in the FFDM + DBT cohort. There was no difference in biopsy rate, PPV1, PPV3, or CDR between the two groups.
The researchers concluded that their findings support the use of SM for patients undergoing screening with DBT.
Burnout in Radiology: Key Risk Factors and Promising Solutions
June 9th 2025Recognizing the daunting combination of increasing imaging volume and workforce shortages, these authors discuss key risk factors contributing to burnout and moral injury in radiology, and potential solutions to help preserve well-being among radiologists.
Mammography AI Platform for Five-Year Breast Cancer Risk Prediction Gets FDA De Novo Authorization
June 2nd 2025Through AI recognition of subtle patterns in breast tissue on screening mammograms, the Clairity Breast software reportedly provides validated risk scoring for predicting one’s five-year risk of breast cancer.
Emerging AI Mammography Model May Enhance Clarity for Initial BI-RADS 3 and 4 Classifications
May 21st 2025In a study involving over 12,000 Asian women, researchers found that an artificial intelligence (AI) model converted over 83 percent of false positives in patients with initial BI-RADS 3 and 4 assessments into benign BI-RADS categories.
