Computer-aided diagnosis (CAD) can help radiologists find early-stage
breast cancers that might otherwise be missed, according to findings
from a retrospective study presented at the Era of Hope
Department of Defense Breast Cancer Research Program meeting.
CAD cannot pick up lesions that are invisible at mammography,
but it can compensate for some cases of radiologist oversight,
said principal investigator Kunio Doi, PhD, professor of radiology at
The University of Chicago. We believe the increasingly positive
results with CAD demonstrate it can serve as a second
opinion for traditional screening mammograms.
Despite mammographys proven value, many small cancers are
barely evident on mammograms and can elude detection by tired or less
experienced radiologists. Computer-assisted diagnosis provides a
measure of insurance against human error by graphically drawing
radiologists attention to masses and microcalcifications.
Missed Cancer Detected
In this study, a CAD Prototype Intelligent Workstation, developed by
Dr. Doi and colleagues at The University of Chicago, was used to
review the mammograms of more than 22,000 women who had been screened
routinely over the past 5 years. Among the first 12,670 women whose
charts were analyzed, 79 developed breast cancer.
Although many of these cancers were eventually detected by
mammography, 23 women had had an earlier screening mammogram that was
reported as negative and, in retrospect, was found to show cancer.
The CAD workstation identified 52% of these missed cancers roughly a
year before they were actually detected.
Radiologists are now using CAD (under subcontract from the
Universitys Department of Defense grant) as a concurrent second
opinion for all screening mammograms at Grant Square Imaging in
Hinsdale, Illinois. Data analysis from that study will begin in about
With a CAD workstation, a laser scanner first transforms the
mammography film into a detailed matrix of digital data.
Microcalcifications appear as tiny white spots, and masses appear as
round or irregular shapes. Guided by complex programming refined over
many years, the systems computer vision and artificial
intelligence algorithms scan the digital matrix, sift out background
findings and normal soft tissue, and then highlight patterns that are
likely to represent lesions.
Areas interpreted as suspicious are flagged on the digital mammogram
with arrows. After reviewing the mammograms and the computer output,
a radiologist prepares a negative or positive report.
The next challenge for CAD is diagnosis, said Dr. Doi.
We have already developed algorithms that guide our system in
distinguishing benign from malignant lesions. I believe that in time,
as we fine-tune those algorithms, CAD will also become an important
tool in helping women avoid unnecessary biopsies in addition to
diagnosing more cancers.