Interval Breast Cancers Can be More Easily Detected by the Use of AI

A recent UCLA study shows that artificial intelligence (AI) could help detect interval breast cancers that develop between routine screenings and before they become more advanced and harder to treat. Better screening practices and earlier treatment can lead to better patient outcomes. Researchers estimate that by incorporating AI into screening, the number of interval breast cancers can be reduced by 30%. 

The Journal of the National Cancer Institute published the study results, which found that AI was able to identify types of interval tumors, visible on mammograms but not significant enough to be identified by the human eye, and therefore missed by radiologists.

According to Dr. Tiffany Yu, Assistant Professor of Radiology at the David Geffen School of Medicine at UCLA and first author of the study, “This finding is important because these interval cancer types could be caught earlier when the cancer is easier to treat. For patients, catching cancer early can make all the difference. It can lead to less aggressive treatment and improve the chances of a better outcome.”

Although similar studies have been carried out in Europe, this research is among the first in the United States to investigate the use of AI for detecting interval breast cancers. Researchers emphasize important distinctions between U.S. and European screening protocols. In the United States, the majority of mammograms are conducted using digital breast tomosynthesis (DBT), commonly referred to as 3D mammography, with annual screenings being the norm. In contrast, European screening programs typically rely on digital mammography (DM), known as 2D mammography, and patients are generally screened every two to three years

The retrospective study analyzed data from nearly 185,000 past mammograms from 2010–2019 that included DM and DBT. From the data, the team looked at 148 cases where a woman was diagnosed with interval breast cancer. 

Key Study Findings:

• The team found that the AI flagged 76% of the mammograms that had been originally read as normal but were later linked to an interval breast cancer.
• It flagged 90% of missed reading error cases where the cancer had been visible on the mammogram but was missed or misinterpreted by the radiologist.
• It caught about 89% of minimal-signs-actionable cancers that showed very subtle signs and could reasonably have been acted upon, as well as 72% of those with minimal-signs-non-actionable that were likely too subtle to prompt action.
• For cancers that were occult or completely invisible on the mammogram, the AI flagged 69% of cases.
• It was somewhat less effective at identifying true interval cancers that were not present at the time of screening but developed later, flagging about 50% of those.

“While we had some exciting results, we also uncovered a lot of AI inaccuracy and issues that need to be further explored in real-world settings,” said Dr. Hannah Milch, assistant professor of Radiology at the David Geffen School of Medicine and senior author of the study. “For example, despite being invisible on mammography, the AI tool still flagged 69% of the screening mammograms that had occult cancers. However, when we looked at the specific areas on the images that the AI marked as suspicious, the AI did not do as good of a job and only marked the actual cancer 22% of the time.”

Additional prospective studies are needed to understand how radiologists would use AI in practice and address key questions in instances where AI flags areas as suspicious that aren’t visible to the human eye, especially when the AI isn’t always accurate in pinpointing the exact location of cancer.

“While AI isn’t perfect and shouldn't be used on its own, these findings support the idea that AI could help shift interval breast cancers toward mostly true interval cancers,” Yu added. “It shows potential to serve as a valuable second set of eyes, especially for the types of cancers that are the hardest to catch early. This is about giving radiologists better tools and giving patients the best chance at catching cancer early, which could lead to more lives saved.”