AI Might Help Spot, Evaluate Prostate Cancer

In another step toward using artificial intelligence in medicine, a new study shows that computers can be trained to match human experts in judging the severity of prostate tumors.

Researchers found that their artificial intelligence system was "near perfect" in determining whether prostate tissue contained cancer cells. And it was on par with 23 "world-leading" pathologists in judging the severity of prostate tumors.

No one is suggesting computers should replace doctors. But some researchers do think AI technology could improve the accuracy and efficiency of medical diagnoses.

Typically, it works like this: Researchers develop an algorithm using "deep learning" -- where a computer system mimics the brain's neural networks. It's exposed to a large number of images -- digital mammograms, for example -- and it teaches itself to recognize key features, such as signs of a tumor.

Earlier this month, researchers reported on an AI system that appeared to best radiologists in interpreting screening mammograms. Other studies have found that AI can outperform doctors in distinguishing harmless moles from skin cancer, and detecting breast tumor cells in lymph node samples.

The new study looked at whether it's possible to train an AI system to detect and "grade" prostate cancer in biopsied tissue samples. Normally, that's the work of clinical pathologists -- specialists who examine tissue under the microscope to help diagnose disease and judge how serious or advanced it is.

It's painstaking work and, to a certain degree, subjective, according to study leader Martin Eklund, a senior researcher at the Karolinska Institute in Sweden.

Then there's the workload. In the United States alone, more than 1 million men undergo a prostate biopsy each year -- producing more than 10 million tissue samples to be examined, Eklund's team noted.

To create their AI system, the researchers digitized more than 8,000 prostate tissue samples from Swedish men ages 50 to 69, creating high-resolution images. They then exposed the system to roughly 6,600 images -- training it to learn the difference between cancerous and noncancerous tissue.

Next came the test phase. The AI system was asked to distinguish benign tissue from cancer in the remaining samples, plus around 300 from men who'd had biopsies at Karolinska. The AI results, the researchers reported, were almost always in agreement with the original pathologist's assessment.

And when it came to grading the severity of prostate tumors with what's called a Gleason score, the AI system was comparable to the judgment of 23 leading pathologists from around the world.

Much work, however, remains. A next step, Eklund said, is to see how the AI system performs across different labs and different pathology scanners, which are used to create digital images.

But one day, he said, AI could be used in a number of ways -- including as a "safety net" to make sure a pathologist didn't miss a cancer. It might also improve efficiency by prioritizing suspicious biopsies that pathologists should examine sooner.

Studies like this are a necessary step toward incorporating AI into medical practice, said Dr. Matthew Hanna, a pathologist at Memorial Sloan Kettering Cancer Center in New York City.

But, he stressed, "there's still a long road ahead."

Hanna, who was not involved in the study, is also a spokesperson for the College of American Pathologists.

Like Eklund, he said that any AI system would have to be validated across different centers, and different pathology scanners. And ultimately, Hanna said, studies will need to show that such technology can be used effectively in pathologists' real-world practice.

There are practical realities, too. At the moment, Hanna pointed out, only a relative minority of pathology labs use digital systems in patient care. That's key because for any AI algorithm to work, there have to be digital images to analyze. Most often, pathologists still study tissue using the classic approach -- glass slides and a microscope.

What's clear is that machines won't be replacing humans -- at least in the foreseeable future.

"This technology is coming," Hanna said. "But as opposed to replacing doctors, it will transform how they deliver care -- hopefully for the better."

The study was reported online January in The Lancet Oncology.

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