New research could help reduce false-positive biopsy rates
Ductal carcinoma in situ (DCIS) calcifications detected on screening mammograms are larger and grow at a faster rate than benign calcifications, according to new findings published in Radiology. These findings, the authors explained, could lead to fewer false-positive biopsies.
“In current practice, biopsy referral of calcifications is based on radiologists’ impressions of the morphology and distribution according to the Breast Imaging-Reporting and Data System (BI-RADS) Atlas,” wrote Lars J. Grimm, MD, MHS, of Duke Radiology in Durham, North Carolina, and colleagues. “These features have limited predictive value, with false-positive biopsy rates for calcifications ranging from 30% to 87%. Furthermore, the interobserver variability for these morphology classifiers is at best moderate. Improved methods to allow differentiation of benign from malignant disease are thus needed.”
Grimm et al. explored data from more than 2,000 calcifications that were later biopsied at a single institution from 2008 to 2015. Mammograms from all DCIS cases were reviewed.
Overall, the study included 74 DCIS calcifications and 148 benign breast calcifications. The median patient age was 62 years old; 61% of the women were white and breast density did not appear to make a significant impact on a calcification’s likelihood of being DCIS or benign.
At the time of patient’s biopsy, the DCIS calcifications had a median size of 10 mm. Benign breast calcifications, meanwhile, had a median size of 6 mm. Also, DCIS calcifications had a higher relative growth rate after adjustment (96%) than benign breast calcifications (68%).
“Our findings show that benign breast disease typically grows over time and hence using a threshold of any growth for intervention will necessarily yield benign biopsies,” the authors wrote. “Additionally, the faster yearly growth rates between DCIS and benign disease could be used in a decision model for biopsy referral. The absolute changes in extent are small, but they should be readily apparent to computer vision algorithms, which in combination with other imaging features could provide more refined prediction models.”
Michael has more than 18 years of experience as a professional writer and editor. He has written at length about cardiology, radiology, artificial intelligence and other key healthcare topics.