Mammography is the most widely used breast cancer screening tool, but diagnosing cancer from these images is a challenge. One in five cases of breast cancer is missed by radiologists and, according to the American Cancer Society, 50 percent of all women who undergo screening for a 10-year period will experience a false positive, in which cancer is wrongly suspected.
A false positive can lead to overtreatment with invasive biopsies and unnecessary stress for patients. A false negative can result in delayed detection and treatment.
An international research team from Google, Northwestern Medicine and two screening centers in the United Kingdom (UK) worked together to build an artificial intelligence (AI) model to address these shortcomings.
The team used fully de-identified mammograms accompanied by biopsy-proven outcomes and longitudinal follow-up to train a deep-learning AI model to identify breast cancer in screening images. The model was tested against a new set of mammograms from the U.K., where screening occurs every three years, and from the U.S., where screening occurs every one to two years. These predictions were then compared against the set of predictions made in clinical practice as well as those gathered from six radiologists in an independent study.
The study was published January 1 in Nature. Key findings are as follows: