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Digital Breast Tomosynthesis May Improve Breast Cancer Screening Performance in Community Settings


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Researchers have found that digital breast tomosynthesis may have improved breast cancer screening performance in community practices and could be effective at identifying more invasive cancers compared with digital mammography, according to a new study published by Lee et al in Radiology. The findings also demonstrated that radiologists’ interpretive performance may have improved when utilizing digital breast tomosynthesis.

Background

After gaining U.S. Food and Drug Administration approval in 2011, digital breast tomosynthesis was rapidly adopted in the United States. As of September 2022, 84% of all mammography screening facilities in the United States included digital breast tomosynthesis units.

Reducing breast cancer mortality through routine screenings is contingent upon radiologists’ interpretive performance. Since the Mammography Quality Standards Act was enacted in 1992, screening facilities and interpreting radiologists have been required to meet minimum quality standards. In 2006, the Breast Cancer Surveillance Consortium—a collaborative network of breast imaging registries conducting research to assess and improve the delivery and quality of breast cancer screenings and related patient outcomes—began publishing screening performance benchmarks.

Study Methods and Results

In the new retrospective study, the researchers collected digital breast tomosynthesis screening exams from five Breast Cancer Surveillance Consortium registries between 2011 and 2018 to establish performance benchmarks for digital breast tomosynthesis screenings and evaluate performance trends over time in U.S. community practices. A total of 896,101 patients undergoing 2,301,766 screening exams—458,175 digital breast tomosyntheses and 1,843,591 two-dimensional digital mammograms—were included in the study.

The researchers noted that these benchmarks included abnormal interpretation rates, cancer detection rates, sensitivity, specificity, and false-negative rates.

Compared with digital mammography screening exams from the same time period and previously published Breast Cancer Surveillance Consortium and National Mammography Database performance benchmarks, all performance benchmarks for digital breast tomosynthesis were higher except for sensitivity and the false-negative rates—which were similar to concurrent and prior digital mammography performance benchmarks.

“Radiologists appear to perform better with digital breast tomosynthesis and have higher accuracy on screening [three-dimensional] mammograms than [two-dimensional] mammograms across U.S. practices,” explained lead study author Christoph I. Lee, MD, MS, MBA, Professor of Radiology at the School of Medicine, Adjunct Professor of Health Systems and Population Health at the School of Public Health, and Director of the Northwest Screening and Cancer Outcomes Research Enterprise at the University of Washington. “As this becomes the mainstay screening modality, we hope to see improved screening outcomes for [patients].”

The researchers found that the abnormal interpretation rate was 8.3%, cancer detection rate was 5.8 per 1,000 exams, sensitivity was 87.4%, and specificity was 92.2%.

With digital breast tomosynthesis, 97.6%, 91.8%, 75.0%, and 74.0% of assessed radiologists achieved the recommended acceptable performance ranges for cancer detection rate, sensitivity, abnormal interpretation rate, and specificity, respectively.

Conclusions

“Our study demonstrated that more radiologists in U.S. community practices are meeting recommended performance standards with digital breast tomosynthesis than they did with digital mammography,” emphasized Dr. Lee. “This is good news for [patients] and breast cancer screenings, as digital breast tomosynthesis has quickly become the most popular breast cancer screening modality in the [United States],” he added.

Further, Dr. Lee noted that the performance benchmarks were also significant for the emerging field of artificial intelligence (AI). “Any new AI tools aimed at improving mammography accuracy will have to revise their own performance goals based on the improved radiologist performance with [digital breast tomosynthesis],” he concluded.

Disclosure: For full disclosures of the study authors, visit pubs.rsna.org.

The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.
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