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 The study, published in the journal Nature on Wednesday, is the
latest to show that artificial intelligence (AI) has the potential
to improve the accuracy of screening for breast cancer, which
affects one in eight women globally.
Radiologists miss about 20% of breast cancers in mammograms, the
American Cancer Society says, and half of all women who get the
screenings over a 10-year period have a false positive result.
The findings of the study, developed with Alphabet Inc's <GOOGL.O>
DeepMind AI unit, which merged with Google Health in September,
represent a major advance in the potential for the early detection
of breast cancer, Mozziyar Etemadi, one of its co-authors from
Northwestern Medicine in Chicago, said.
The team, which included researchers at Imperial College London and
Britain's National Health Service, trained the system to identify
breast cancers on tens of thousands of mammograms.
They then compared the system's performance with the actual results
from a set of 25,856 mammograms in the United Kingdom and 3,097 from
the United States.
The study showed the AI system could identify cancers with a similar
degree of accuracy to expert radiologists, while reducing the number
of false positive results by 5.7% in the U.S.-based group and by
1.2% in the British-based group.
It also cut the number of false negatives, where tests are wrongly
classified as normal, by 9.4% in the U.S. group, and by 2.7% in the
British group.
These differences reflect the ways in which mammograms are read. In
the United States, only one radiologist reads the results and the
tests are done every one to two years. In Britain, the tests are
done every three years, and each is read by two radiologists. When
they disagree, a third is consulted.
'SUBTLE CUES'
In a separate test, the group pitted the AI system against six
radiologists and found it outperformed them at accurately detecting
breast cancers.
Connie Lehman, chief of the breast imaging department at Harvard's
Massachusetts General Hospital, said the results are in line with
findings from several groups using AI to improve cancer detection in
mammograms, including her own work.
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The notion of using computers to improve cancer diagnostics is
decades old, and computer-aided detection (CAD) systems are
commonplace in mammography clinics, yet CAD programs have not
improved performance in clinical practice.
The issue, Lehman said, is that current CAD programs were trained to
identify things human radiologists can see, whereas with AI,
computers learn to spot cancers based on the actual results of
thousands of mammograms.
This has the potential to "exceed human capacity to identify subtle
cues that the human eye and brain aren't able to perceive," Lehman
added.
Although computers have not been "super helpful" so far, "what we've
shown at least in tens of thousands of mammograms is the tool can
actually make a very well-informed decision," Etemadi said.
The study has some limitations. Most of the tests were done using
the same type of imaging equipment, and the U.S. group contained a
lot of patients with confirmed breast cancers.
Crucially, the team has yet to show the tool improves patient care,
said Dr Lisa Watanabe, chief medical officer of CureMetrix, whose AI
mammogram program won U.S. approval last year.
"AI software is only helpful if it actually moves the dial for the
radiologist," she said.
Etemadi agreed that those studies are needed, as is regulatory
approval, a process that could take several years.
(Reporting by Julie Steenhuysen in Chicago; Editing by Alexander
Smith and Matthew Lewis)
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