The research team trained the algorithm by gathering images from more than a million stained tissue slides from patient biopsies. Pathologists then labeled each image so the algorithm could differentiate healthy and malignant tissue.
The algorithm then assessed 1,600 slides from 100 UPMC patients who had been suspected of having prostate cancer. According to study results published in The Lancet Digital Health, the algorithm exhibited 98 percent sensitivity and 97 percent specificity when detecting prostate cancer. It even spotted six potentially malignant slides that expert pathologists failed to flag initially.
The algorithm is the first of its kind to take its analysis beyond cancer identification, as it can also accurately categorize tumors by grade.
“Algorithms like this are especially useful in lesions that are atypical,” one of the study’s authors, Rajiv Dhir, MD, said in a news release. “A nonspecialized person may not be able to make the correct assessment. That’s a major advantage of this kind of system.”
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