The Addition of Automated Breast Ultrasound to Mammography in Breast Cancer Screening Decreases Stage at Diagnosis.

Abstract

RATIONALE AND OBJECTIVES This study aimed to determine the best screening strategy using automated whole-breast ultrasound and mammography in women with increased breast density or an elevated risk of breast cancer. MATERIALS AND METHODS After an institutional review board waiver was obtained, a retrospective review of 122 cancer cases diagnosed in 3435 women with increased breast density or an elevated risk of breast cancer, screened with mammography and supplemental automated whole-breast ultrasound, was performed. The imaging modality on which each cancer was seen was noted. Screening strategies were postulated. For each screening strategy, rates of advanced cancer diagnosis, with 95% confidence limits, are calculated using the Clopper-Pearson method. Differences in outcomes were calculated using Cochrane Q test and McNemar test for paired observations. Results were expressed for all stages of cancer and for invasive cancers only. RESULTS When all cancer stages are considered, mammographic screening reduces advanced cancers by 31% over no screening. Ultrasound-only screening results in a 32% reduction. The combination of mammographic and ultrasound screening reduces advanced cancers by 40% (P < .05). Compared to mammographic screening, mammographic plus ultrasound screening reduces advanced-stage cancers by 5.7% (P = 0.03) for all stages and 10.8% (P = 0.02) for invasive cancers. CONCLUSIONS For women with increased breast density or who are at high risk of developing breast cancer, a combination of screening mammography and whole-breast automated ultrasound is superior to mammographic screening. Screening ultrasound alone is also an effective screening strategy.

DOI: 10.1016/j.acra.2017.06.014

Cite this paper

@article{Grady2017TheAO, title={The Addition of Automated Breast Ultrasound to Mammography in Breast Cancer Screening Decreases Stage at Diagnosis.}, author={Ian Grady and Nailya Chanisheva and Tony Vasquez}, journal={Academic radiology}, year={2017} }