What New Research Reveals About the Impact of AI and DBT Screening: An Interview with Manisha Bahl, MD

While there has been a fair amount of research examining the impact of artificial intelligence (AI) with two-dimensional mammography, there has been relatively limited data on the use of AI with digital breast tomosynthesis (DBT), noted Manisha Bahl, M.D., MPH, FSBI, in a recent interview.

Dr. Bahl also noted there has been little data in the AI computer-assisted diagnosis (CAD) breast imaging research with respect to what specific breast cancers are detected or not detected with AI.

“The purpose of our (new) study was to fill that gap in knowledge or understanding by providing an analysis of the types of breast cancers that are missed versus detected by a commercial AI based CAD algorithm for tomosynthesis,” explained Dr. Bahl, an associate professor of radiology at Harvard Medical School.

For the study, which was recently presented at the Society for Breast Imaging (SBI) conference, Dr. Bahl and colleagues assessed the Genius AI Detection 2.0 software (Hologic) in 5,000 patients who had DBT screening exams. They found that the AI software detected and correctly localized 90 percent of true-positive cases.

Dr. Bahl noted the AI software was more likely to detect invasive ductal carcinoma and lymph node positive carcinoma. The AI software was less likely to detect true positive cancers that presented as asymmetries on mammography as well as invasive lobular carcinoma, according to Dr. Bahl.

“These results suggest that the AI algorithm is optimized to detect clinically significant lymph node positive cancers (and) may require further training to identify invasive lobular cancers, which are known to be difficult to detect because of their unique infiltrative growth pattern,” posited Dr. Bahl, the director of the breast imaging fellowship program at Massachusetts General Hospital.

Dr. Bahl also said she was impressed by the ability of the AI software to detect 32 percent of the false negative cases in the study.

“I think those results suggest that integrating AI into our practice could potentially help decrease the false negative rate of screening tomosynthesis, which is an important metric, an important surrogate marker for long term outcomes. Those results are very promising to me and show that AI has the potential to improve our quality in addition to our efficiency,” noted Dr. Bahl.

(Editor’s note: For related content, see “Multicenter Mammography Study Shows Greater than 10 Percent Increase in Breast Cancer Detection with Adjunctive AI,” “New Mammography Studies Assess Image-Based AI Risk Models and Breast Arterial Calcification Detection” and “Can AI Bolster Breast Cancer Detection in DBT Screening?”)

For more insights from Dr. Bahl, watch the video below.