AI catches one-third of interval breast cancers missed at screening
An AI algorithm for breast cancer screening has potential to enhance the performance of digital breast tomosynthesis (DBT), reducing interval cancers by up to one-third, according to a study published in Radiology, a journal of the Radiological Society of North America (RSNA).
Interval breast cancers - symptomatic cancers diagnosed within a period between regular screening mammography exams - tend to have poorer outcomes due to their more aggressive biology and rapid growth. DBT, or 3D mammography, can improve visualization of breast lesions and reveal cancers that may be obscured by dense tissue. Because DBT is relatively new as an advanced screening technology, long-term data on patient outcomes are limited in institutions that have not transitioned to DBT until recently.
"Given the lack of long-term data on breast cancer-related mortality measured over 10 or more years following the initiation of DBT screening, the interval cancer rate was often used as a surrogate marker," explained study author Manisha Bahl, M.D., M.P.H., breast imaging division quality director and co-service chief at Massachusetts General Hospital and associate professor at Harvard Medical School. "Lowering this rate is assumed to reduce breast cancer-related morbidity and mortality."
In a study of 1,376 cases, Dr. Bahl and her colleagues retrospectively analysed 224 interval cancers in 224 women who had undergone DBT screening. On those DBT exams, the AI algorithm (Lunit INSIGHT DBT v1.1.0.0) correctly localized 32.6% (73/224) of cancers that were previously undetected.
"My team and I were surprised to find that nearly one-third of interval cancers were detected and correctly localized by the AI algorithm on screening mammograms that had been interpreted as negative by radiologists, highlighting AI’s potential as a valuable second reader," Dr. Bahl said.
Source: Digital Health News, 1 August 2025