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February 2025 | George M. Pikler, M.D., Ph.D., FACP, Lead Oncology Advocate N1X10

More Breast Tumors Are Caught Using AI

Mammography screening programs contribute to reducing mortality associated with breast cancer. They identify women who are unlikely to have cancer and those who have findings that require further investigation.

Abnormal or uncertain results on mammograms are common. False-positive mammogram results are apparent abnormalities that, after further evaluation, are found not to be cancer. They are more common among certain groups, including younger women, women with dense breasts, women who have had previous breast biopsies, and women with a family history of breast cancer. In the United States, about 10% of mammograms lead to a woman being called back for further testing. Of those mammograms, however, only about 7% lead to a diagnosis of cancer. False-positive results can lead to women having additional imaging or invasive procedures, including biopsies of the breast. The additional testing can be time-consuming, costly, and also have psychological and physical effects for women.

In a recent report, (1) the addition of artificial intelligence (AI) in mammography screening has shown an increase in the specificity of the screening by minimizing false-positive results. In the study, almost half a million women in Germany were screened for breast cancer by 119 radiologists. Around half of participants had an AI tool used as part of the screening process. The breast-cancer detection rate was 6.7 per 1,000 women in the AI-supported group and 5.7 per 1,000 women in the control group. Identifying one additional breast cancer for every 1,000 women using AI was “extremely positive and exceeded our expectations”, said epidemiologist and co-author Alexander Katalinic. “We can now demonstrate that AI significantly improves the cancer detection rate in screening for breast cancer.”

The implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand.

(1) NatureMedicine. 2025, January 7
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Erica

Erica Cross, PA

PA

Erica is a board certified Physician Assistant. She obtained her Master’s degree in Physician Assistant studies from Our Lady of the Lake College in Baton Rouge, LA. She began practicing in 2011 and has worked clinically in Orthopedics and Dermatology. The majority of her career has been spent in a Dermatology practice where she assisted in Mohs surgery, treating various types of skin cancer. She also teaches in the medical simulation department at the University of South Alabama and enjoys every aspect of medical education.