Scientists have designed an artificial intelligence(AI)-based tool that can rapidly decode a brain tumour’s DNA to determine its molecular identity during surgery, guiding surgical and treatment decisions.

Knowing a tumour’s molecular type can enable neurosurgeons to make decisions such as how much brain tissue to remove and whether to place tumour-killing drugs directly into the brain — while the patient is still on the operating table.

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Under the current approach, it can take a few days and up to a few weeks. Removing too much when the tumour is less aggressive can affect a patient’s neurologic and cognitive function.

Likewise, removing too little when the tumour is highly aggressive may leave behind malignant tissue that can grow and spread quickly.

“Right now, even state-of-the-art clinical practice cannot profile tumours molecularly during surgery. Our tool overcomes this challenge by extracting thus-far untapped biomedical signals from frozen pathology slides,” said Kun-Hsing Yu, Assistant Professor of biomedical informatics in the Blavatnik Institute at Harvard Medical School in the US.

“The ability to determine intraoperative molecular diagnosis in real time, during surgery, can propel the development of real-time precision oncology,” Yu added.

The tool, called CHARM (Cryosection Histopathology Assessment and Review Machine), ability to expedite molecular diagnosis could be particularly valuable in areas with limited access to technology to perform rapid cancer genetic sequencing.

CHARM was developed using 2,334 brain tumour samples from 1,524 people with glioma from three different patient populations. When tested on a never-before-seen set of brain samples, the tool distinguished tumours with specific molecular mutations at 93 per cent accuracy and successfully classified three major types of gliomas with distinct molecular features that carry different prognoses and respond differently to treatments.

Further, the tool successfully captured visual characteristics of the tissue surrounding the malignant cells. It was also capable of spotting telltale areas with greater cellular density and more cell death within samples, both of which signal more aggressive glioma types.

CHARM was also able to pinpoint clinically important molecular alterations in a subset of low-grade gliomas, a subtype of glioma that is less aggressive and therefore less likely to invade surrounding tissue. Each of these changes also signals different propensity for growth, spread, and treatment response.

The researchers said that while the model was trained and tested on glioma samples, it could be successfully retrained to identify other brain cancer subtypes.

However, CHARM still has to be clinically validated through testing in real-world settings and cleared by the US FDA before deployment in hospitals, the research team said, in the study published in the journal Med.