Researchers at the University of Chicago have developed a computer technique that “learns” how benign and malignant breast calcifications appear on digital mammograms so not only can it detect them, but it can also predict the likelihood that the calcifications are associated with cancer.
“In this study, we analyzed 49 full-field digital mammograms, 19 of which showed cancer,” said Rich Rana, a medical student at the University of Chicago. Four mammography specialists read the images and electronically put a box around the suspicious calcifications. The computer then automatically detected the calcifications within the box, analyzed them and calculated the probability of cancer, Rana said.
The system proved to consistently achieve performance comparable to the radiologists in classifying malignant and benign calcifications, regardless of who was using it, Rana added. One technique for rating the computer’s effectiveness is to give it one malignant case and one benign case and then test its ability to determine which is which, Rana said. Using this technique, the radiologist had a 72% chance of making the correct diagnosis, and the computer had a 79% chance.
Keri J. Sperry | ARRS
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