Absence of an anticipated racial disparity in interval breast cancer within a large health care organization

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

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.

Validation of the breast cancer surveillance consortium model of breast cancer risk

PURPOSE

In order to use a breast cancer prediction model in clinical practice to guide screening and prevention, it must be well calibrated and validated in samples independent from the one used for development. We assessed the accuracy of the breast cancer surveillance consortium (BCSC) model in a racially diverse population followed for up to 10 years.

METHODS

The BCSC model combines breast density with other risk factors to estimate a woman's 5- and 10-year risk of invasive breast cancer. We validated the model in an independent cohort of 252,997 women in the Chicago area. We evaluated calibration using the ratio of expected to observed (E/O) invasive breast cancers in the cohort and discrimination using the area under the receiver operating characteristic curve (AUROC).

RESULTS

In an independent cohort of 252,997 women (median age 50 years, 26% non-Hispanic Black), the BCSC model was well calibrated (E/O = 0.94, 95% confidence interval [CI] 0.90-0.98), but underestimated the incidence of invasive breast cancer in younger women and in women with low mammographic density. The AUROC was 0.633, similar to that observed in prior validation studies.

CONCLUSIONS

The BCSC model is a well-validated risk assessment tool for breast cancer that may be particularly useful when assessing the utility of supplemental screening in women with dense breasts.