A risk-based algorithm could help maximize breast cancer detection rates during times of crisis when mammogram capacity is limited, research published March 25 in JAMA Network Open suggests.
Between 2014 and 2019, researchers from Sacramento-based UC Davis Health and Kaiser Permanente Washington Health Research in Seattle analyzed data from nearly 900,000 people and nearly 2million mammograms. They considered whether a risk-based algorithm that took into account clinical indication, breast cancer symptoms, breast cancer history and age could optimize cancer detection.
Findings showed the approach was successful: 12 percent of mammograms with "very high" and "high" cancer detection rates accounted for 55 percent of detected cancers, while 44 percent of mammograms with "very low" detection rates made up 13 percent of detected cancers.
"What this means is that triaging individuals most likely to have cancer during periods of reduced capacity may be effective at detecting the most cancers with the fewest mammograms," said Diana Miglioretti, PhD, one of the study's lead authors and division chief of biostatistics at UC Davis' Department of Public Health Sciences. "Whether it is due to unexpected new surges in COVID-19 cases prompting lockdowns or other emergencies such as cyberattacks or natural disasters like wildfires, the study results provide important guidance for navigating through any crisis that could impact the availability of mammograms."