Accurate and Cost-Effective Sensitivity (ACES) Algorithm for Improved Breast Cancer Screening Metrics

ACES map

This study, led by collaborators at University of Wisconsin-Madison, is designed to develop new statistical approaches to improve the accuracy of breast cancer screening performance reports for radiology facilities and radiologists. Radiology facility breast cancer screening performance reports typically only include cancer diagnosis data available from the facility at which the screening mammography was performed. However, some women will seek diagnostic care at other healthcare facilities and may be diagnosed with cancer at other healthcare facilities. These cases “lost to follow-up” bias the screening performance report.

We will test a range of analytic approaches that account for this incomplete cancer capture and can be applied to data readily available at screening centers. We will develop and test new approaches, using existing data from the University of Wisconsin-Madison, University of California-Davis, and the University of Vermont Breast Cancer Surveillance System. UVM will participate in the development of the statistical approaches, including testing of the algorithms on data from the Vermont Breast Cancer Surveillance System.

This project is funded by grant AWD00001647 from the American Cancer Society (ACS).