Scientists have designed an AI tool that can rapidly decode a brain tumor’s DNA to determine its molecular identity during surgery — critical information that under the current approach can take a few days and up to a few weeks.
Knowing a tumor’s molecular type enables neurosurgeons to make decisions such as how much brain tissue to remove and whether to place tumor-killing drugs directly into the brain — while the patient is still on the operating table.
A report on the work, led by Harvard Medical School researchers, is published July 7 in the journal Med.
Accurate molecular diagnosis — which details DNA alterations in a cell — during surgery can help a neurosurgeon decide how much brain tissue to remove. Removing too much when the tumor is less aggressive can affect a patient’s neurologic and cognitive function. Likewise, removing too little when the tumor is highly aggressive may leave behind malignant tissue that can grow and spread quickly.
Knowing a tumor’s molecular identity during surgery is also valuable because certain tumors benefit from on-the-spot treatment with drug-coated wafers placed directly into the brain at the time of the operation, Yu said.
The tool, called CHARM (Cryosection Histopathology Assessment and Review Machine), is freely available to other researchers. It still has to be clinically validated through testing in real-world settings and cleared by the FDA before deployment in hospitals, the research team said.
CHARM was developed using 2,334 brain tumor 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 tumors with specific molecular mutations at 93 percent accuracy and successfully classified three major types of gliomas with distinct molecular features that carry different prognoses and respond differently to treatments.
Going a step further, the tool successfully captured visual characteristics of the tissue surrounding the malignant cells. It was capable of spotting telltale areas with greater cellular density and more cell death within samples, both of which signal more aggressive glioma types.
The tool 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 tool further connected the appearance of the cells — the shape of their nuclei, the presence of edema around the cells — with the molecular profile of the tumor. This means that the algorithm can pinpoint how a cell’s appearance relates to the molecular type of a tumor.
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