Operating room technology
“If they’re not quite certain, surgeons are usually a bit more conservative in what they do during surgery, but in some cases that means the patient will need to have a second surgery later on,” explains Bastiaan Tops, head of the Laboratory for Paediatric Oncology at the Princess Máxima Center in Utrecht, the Netherlands. “Or you did too much – you excised everything, while in hindsight, you could have been more conservative.” Together with collaborators at UMC Utrecht,
Tops has been working on an AI-based solution that can dramatically speed up diagnosis. Sturgeon, the algorithm in question, can identify the tumour subtype within 20 to 40 minutes – fast enough for the surgeon to adjust their strategy mid-procedure. “The algorithm is now being used in practice around two to three times a week,” says Jeroen de Ridder, research group leader within UMC Utrecht and the Oncode Institute. “If you think about it, that’s terrible because that means there are three paediatric brain surgeries every week in a small country like the Netherlands. But we hope that with this algorithm, we can provide the surgeon with more information to make solid decisions.”
Building on what’s gone before The research builds on work by a team in Oslo, which used nanopore sequencing – a method for analysing DNA in real time – to classify brain tumours. By looking at methylation, genetic modifications in the DNA that vary depending on the subtype, they were able to make accurate diagnoses very rapidly. Reading their paper, Tops had a hunch it might be possible to go one step further.
“The Oslo team built a specific algorithm for each specific patient, based on the data they sequenced at that point in time,” he explains. “The problem with that is that you need to retrain an algorithm during surgery, which takes computing time, and you can never validate that algorithm because it was trained only on those specific data.” He approached De Ridder’s team with a question: might it be possible to create a single, patient- agnostic algorithm? They set to work on a solution that can be used for any patient and requires only a small amount of sequencing data. “If I show you an image of, say, an elephant, but I only give you 1% of the pixels in the image, how can you see the elephant?” says De Ridder. “That’s what we solved with the AI. We don’t know beforehand which part of the genome we are measuring, and we know we only have time to measure a tiny fraction of it. So we have made an AI that doesn’t care which data it receives, as long as it gets a little bit – it can still make a robust decision.” The algorithm was trained using microarray data available in public repositories and validated using data from the Princess Máxima Center biobank. De
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www.practical-patient-care.com
Ridder’s team ran the algorithm through the data over 30 million times before retrospectively applying the model to 50 brain tumour samples, 45 of which were classified accurately within 20–40 minutes. “It’s kind of a black box – we don’t know exactly what the algorithm sees or what the decision is based on,” says Tops. “That means training and thoroughly validating the algorithm is of crucial importance. Thanks to the biobank samples, we now have a pre-trained algorithm that we can run in minutes, which can be continuously validated to see how well it performs.”
There are around 80 different brain tumour subtypes, each with a different molecular profile.
“It’s kind of a black box – we don’t know exactly what the algorithm sees or what the decision is based on. That means training and thoroughly validating the algorithm is of crucial importance.” Bastiaan Tops, Laboratory for Pediatric Oncology
Although the algorithm is now being used successfully across both paediatric and adult brain surgeries, there is still a place for a human pathologist. He or she will examine the sample through the microscope and communicate their findings to the surgeon, who will interpret both sets of results as a package deal.
Opportunities and risks While it’s still early days for this research, and larger studies are needed to assess its impact on patient outcomes, the work is generating a buzz. Simon Williams, a neurosurgical trainee who was not involved in the research, describes the paper as “excellent”. “It showcases exactly how AI can positively impact our field, by reducing diagnostic times from days to minutes,” says Williams, who was previously a clinical research fellow in neurosurgery, AI and robotics at the Wellcome/EPSRC Centre for
20-40
The time, in minutes, taken to identify tumour subtypes during surgery by the AI-based algorithm.
Sturgeon 47
Sezer33/
Shutterstock.com
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