Researchers in the Netherlands have developed a quicker and more precise method of diagnosing brain tumour with artificial intelligence, guiding surgeons’ scope of operation.
The method, described in a study published on Wednesday in the journal Nature, involves a computer scanning segments of a tumour’s DNA and alighting on certain chemical changes that can yield a detailed diagnosis of the type and even subtype of the brain tumour.
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The diagnosis, generated during the early stages of an hour-long surgery, can help surgeons decide how aggressively to operate, the researchers said.
In the future, the method may also help steer doctors toward treatments tailored for a specific subtype of tumour.
“It’s imperative that the tumour subtype is known at the time of surgery,” said Jeroen de Ridder, an associate professor in the Centre for Molecular Medicine at UMC Utrecht, a Dutch hospital, who helped lead the study.
“What we have now uniquely enabled is to allow this very fine-grained, robust, detailed diagnosis to be performed already during the surgery.”
Their deep learning system, called Sturgeon, was first tested on frozen tumour samples from previous brain cancer operations.
It accurately diagnosed 45 of 50 cases within 40 minutes of starting genetic sequencing. In the other five cases, it refrained from offering a diagnosis because the information was unclear.
The system was then tested during 25 live brain surgeries, most of them on children, alongside the standard method of examining tumour samples under a microscope.
The new approach delivered 18 correct diagnoses and failed to reach the needed confidence threshold in the other seven cases.
It turned around its diagnoses in less than 90 minutes, the study reported — short enough for it to inform decisions during an operation.
Currently, in addition to examining brain tumour samples under a microscope, doctors can send them for more thorough genetic sequencing.
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The new method uses a faster genetic sequencing technique and applies it only to a small slice of the cellular genome, allowing it to return results before a surgeon has started operating on the edges of a tumour.
De Ridder said that the model was powerful enough to deliver a diagnosis with sparse genetic data, akin to someone recognising an image based on only one percent of its pixels, and from an unknown portion of the image.
“It can figure out itself what it’s looking at and make a robust classification,” said de Ridder, who is also a principal investigator at Oncode Institute, a cancer research centre in the Netherlands.
