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Computer Technology Helps Radiologists Spot Overlooked Small Breast Cancers

Computer Technology Helps Radiologists Spot Overlooked Small Breast Cancers

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 mammography’s 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 University’s 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 2 years.

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 system’s 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.”

 
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