The "Sweet Spot" Revisited: Optimal Recall Rates for Cancer Detection With 2D and 3D Digital Screening Mammography in the Metro Chicago Breast Cancer Registry


Advocate Health Care


OBJECTIVE. One central question pertaining to mammography quality relates to discerning the optimal recall rate to maximize cancer detection while minimizing unnecessary downstream diagnostic imaging and breast biopsies. We examined the trade-offs for higher recall rates in terms of biopsy recommendations and cancer detection in a single large health care organization.

MATERIALS AND METHODS. We included 2D analog, 2D digital, and 3D digital (tomosynthesis) screening mammography examinations among women 40-79 years old performed between January 1, 2005, and December 31, 2017, with cancer follow-up through 2018. There were 36, 67, and 38 radiologists who read at least 1000 2D analog examinations, 2D digital examinations, and 3D tomosynthesis examinations, respectively, who were included in these analyses. Using logistic regression with marginal standardization, we estimated radiologist-specific mean recall (abnormal interpretations/1000 mammograms), biopsy recommendation, cancer detection (screening-detected in situ and invasive cancers/1000 mammograms), and minimally invasive cancer detection rates while adjusting for differences in patient characteristics.

RESULTS. Among 1,060,655 screening mammograms, the mean recall rate was 10.7%, the cancer detection rate was 4.0/1000 mammograms, and the biopsy recommendation rate was 1.60%. Recall rates between 7% and 9% appeared to maximize cancer detection while minimizing unnecessary biopsies.

CONCLUSION. The results of this investigation are in contrast to those of a recent study suggesting appropriateness of higher recall rates. The "sweet spot" for optimal cancer detection appears to be in the recall rate range of 7-9% for both 2D digital mammography and 3D tomosynthesis. Too many women are being called back for diagnostic imaging, and new benchmarks could be set to reduce this burden.

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