In a recent study from the University of Pittsburgh and elsewhere, scientists showed the highest accuracy to date in recognizing and characterizing prostate cancer using an artificial intelligence (AI) program using an artificial intelligence (AI) program.
Humans are good at recognizing anomalies, but they have their own biases or past experience.
To train the AI to recognize prostate cancer, the team provided images from more than a million parts of stained tissue slides taken from patient biopsies.
Each image was labeled by expert pathologists to teach the AI how to discriminate between healthy and abnormal tissue.
The algorithm was then tested on a separate set of 1,600 slides taken from 100 consecutive patients seen at UPMC for suspected prostate cancer.
During testing, the AI demonstrated 98% sensitivity and 97% specificity in detecting prostate cancer—significantly higher than previously reported for algorithms working from tissue slides.
Also, this is the first algorithm to extend beyond cancer detection, reporting high performance for tumor grading, sizing, and invasion of the surrounding nerves.
These all are clinically important features required as part of the pathology report.
In addition, AI also flagged six slides that were not noted by the expert pathologists.
But the team explained that this doesn’t necessarily mean that the machine is superior to humans.
For example, in the course of evaluating these cases, the pathologist could have simply seen enough evidence of malignancy elsewhere in that patient’s samples to recommend treatment.
For less experienced pathologists, though, the algorithm could act as a failsafe to catch cases that might otherwise be missed.
While these results are promising, the team cautions that new algorithms will have to be trained to detect different types of cancer. The pathology markers aren’t universal across all tissue types.
But they didn’t see why that couldn’t be done to adapt this technology to work with breast cancer, for example.
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The study was published in The Lancet Digital Health and conducted by Rajiv Dhir et al.
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