Slashdot Mirror
Computer Detection Effective In Spotting Cancer
Anti-Globalism notes a large study out of the UK indicating that computer-aided detection can be as effective at spotting breast cancer as two experts reading the x-rays. Mammograms in Britain are routinely checked by two radiologists or technicians, which is thought to be better than a single review (in the US only a single radiologist reads each mammogram). In a randomized study of 31,000 women, researchers found that a single expert aided by a computer does as well as two pairs of eyes. CAD spotted nearly the same number of cancers, 198 out of 227, compared to 199 for the two readers. "In places like the United States, 'Where single reading is standard practice, computer-aided detection has the potential to improve cancer-detection rates to the level achieved by double reading,' the researchers said."
Most results are presented via ROC curve (for the uninitiated, this is a curve that plots true positive rate against false positive rate based on some threshold for classifying a lesion), so the FPR can theoretically be reduced if you're willing to lose sensitivity as well.
The thing is, the outcomes are not balanced. The risk of missing a cancer is considered far greater than the risk of returning a false positive, so the algorithms are usually created with sensitivity rather than specificity in mind. In my opinion (and since I work on some of these algorithms, my opinion is important :)), this is as it should be, and we should worry about specificity only if we can keep a comparable level of sensitivity.
In any case, the article Yahoo is sourcing from does mention the specificity (which is 1-false positive rate), and it is encouraging: with CAD, the specificity was 96.9%, vs. 97.4% for double reading. Given that sensitivity was also similar (87.2% vs. 87.7%), this article paints CAD in a very favorable light.