AI-Assisted Breast Cancer Screening May Reduce Unnecessary Testing

Researchers at Washington University School of Medicine in St. Louis and Whiterabbit.ai, a Silicon Valley-based technology startup, used artificial intelligence (AI) to supplement radiologists’ evaluations of mammograms. The study found that AI may improve breast cancer screening by reducing false positives without missing cases of cancer.
 
An algorithm was developed to identify normal mammograms with very high sensitivity. A simulation was run on patient data to see what would have happened if the very low-risk mammograms had been taken care of by the AI, freeing the doctors to concentrate on the more questionable scans. 
 
The simulation revealed that fewer people would have been called back for additional testing but that the same number of cancer cases would have been detected.
 
The study was published in the journal Radiology: Artificial Intelligence on April 10th.
 
“False positives are when you call a patient back for additional testing, and it turns out to be benign,” explained senior author Richard L. Wahl, MD, a professor of radiology at Washington University’s Mallinckrodt Institute of Radiology (MIR) and a professor of radiation oncology. “That causes a lot of unnecessary anxiety for patients and consumes medical resources. This simulation study showed that very low-risk mammograms can be reliably identified by AI to reduce false positives and improve workflows.”
 
Wahl and colleagues at Whiterabbit.ai used AI to evaluate mammograms to evaluate for cancer. AI model was trained on 123,248 2D digital mammograms (containing 6,161 showing cancer) that were collected and read by Washington University radiologists. The AI model was then tested and validated on three independent sets of mammograms, two from institutions in the U.S. and one in the United Kingdom.
 
The researchers found out how many patients were called back for secondary screening and biopsies, the results of those tests, and the final determination in each case. The AI was then applied to the datasets to test what would have been different if AI had been used to remove negative mammograms in the initial assessments.
 
The largest dataset contained 11,592 mammograms. When scaled to 10,000 mammograms (to make the math simpler for the simulation), the AI identified 34.9% as negative. This would have reduced the callbacks for diagnostic exams by 23.7%.
 
Both the AI rule-out and real-world standard-of-care approaches identified the same 55 cancer cases. This study suggests that out of the 10,000 people who underwent initial mammograms, 262 could have avoided diagnostic exams, and 10 could have avoided biopsies, without any cancer cases being missed.
 
“At the end of the day, we believe in a world where the doctor is the superhero who finds cancer and helps patients navigate their journey ahead,” said co-author Jason Su, co-founder and chief technology officer at Whiterabbit.ai. “The way AI systems can help is by being in a supporting role. By accurately assessing the negatives, it can help remove the hay from the haystack so doctors can find the needle more easily. This study demonstrates that AI can potentially be highly accurate in identifying negative exams. More importantly, the results showed that automating the detection of negatives may also lead to a tremendous benefit in the reduction of false positives without changing the cancer detection rate.”