AI tool improves breast cancer imaging accuracy

Ultrasound exams use high-frequency sound waves passing through tissue to construct real-time images of breast or other tissues. Although not generally used as a breast cancer screening tool, it has served as an alternative to mammography or follow-up diagnostic tests for many women, says Dr. Geras, an assistant professor of radiology at NYU Grossman School of Medicine and a member of Perlmutter Cancer Center.

Ultrasound is cheaper, more widely available in community clinics, and does not involve exposure to radiation, the researchers say. Moreover, ultrasound is better than mammography for penetrating dense breast tissue and distinguishing packed but healthy cells from compact tumors. However, the technology has also been found to result in too many false diagnoses of breast cancer, producing anxiety and unnecessary procedures for women. Some studies have shown that a majority of breast ultrasound exams indicating signs of cancer turn out to be noncancerous after biopsy. “If our efforts to use machine learning as a triaging tool for ultrasound studies prove successful, ultrasound could become a more effective tool in breast cancer screening, especially as an alternative to mammography, and for those with dense breast tissue,” says study co-investigator and radiologist Linda Moy, MD. “Its future impact on improving women’s breast health could be profound,” adds Dr. Moy, a professor of radiology at NYU Grossman School of Medicine and a member of Perlmutter Cancer Center.