Could artificial intelligence help cure brain cancer?

By Mark Waghorn via SWNS

Brain cancer could be cured by artificial intelligence, according to new research.

Scientists have developed a tool that decodes a tumor's DNA - while the patient is under the knife.

It opens the door to personalized treatments for one of the most lethal forms of the disease.

Senior author Dr. Kun-Hsing Yu, of Harvard Medical School, Boston, said: "Right now, even state-of-the-art clinical practice cannot profile tumors molecularly during surgery.

"Our tool overcomes this challenge by extracting thus-far untapped biomedical signals from frozen pathology slides."

The breakthrough could improve survival rates for those with the most lethal, known as glioblastomas. Targeted therapies are urgently needed.

Currently, the critical information is processed during laboratory tests - which can take weeks.

Knowing a tumor's molecular type enables neurosurgeons to make decisions such as whether to place drugs directly into the brain - while the patient is still on the operating table.

It details DNA alterations in a cell. Removing too much tissue when the tumor is less aggressive can affect physical and cognitive function.

Likewise, taking too little when it is spreading fast may leave behind malignant cells that can grow quickly.

Dr. Yu said: "The ability to determine intraoperative molecular diagnosis in real-time, during surgery, can propel the development of real-time precision oncology."

The standard approach used now involves taking brain tissue, freezing it and examining it under a microscope.

A major drawback is the sample tends to alter the appearance of cells - interfering with the accuracy of clinical evaluation.

Furthermore, the human eye, even when using potent scanners, cannot reliably detect subtle genomic variations on a slide.

The AI device, called CHARM (Cryosection Histopathology Assessment and Review Machine) has the potential to overcome these problems.

The team hopes it will be approved by the US Food and Drug Administration for use in hospitals once validated through clinical testing in real-world settings.

Advances in genomics have allowed pathologists to differentiate the molecular signatures of various and specific brain cancers.

Glioblastomas are the most common - as well as the deadliest. There are three main types that have different propensities for growth and spread.

Around 2,500 cases are diagnosed in the UK each year. Only seven percent of patients survive. Last year the disease claimed the life of The Wanted singer Tom Parker.

Other victims around the world have included Senator John McCain, President Biden’s son Beau, actors Robert Forster and Tim Conway and famed film critic Gene Siskel.

Most patients succumb within two years and few make it past five, a statistic that hasn’t improved in decades.

CHARM's ability could be particularly valuable in areas with limited access to technology to perform rapid genetic sequencing.

It provides clues about a tumor's aggressiveness, behavior and likely response to various treatments - informing post-operative decisions.

CHARM was developed using 2,334 brain tumor samples from 1,524 people with glioblastoma from three different patient populations.

When tested the tool distinguished tumors with specific molecular mutations at 93 percent accuracy and successfully classified three major types of glioblastoma that respond differently to treatments.

It also 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 forms.

The tool pinpointed clinically important molecular alterations in less aggressive low-grade glioblastomas that are less likely to invade surrounding tissue.

It could be successfully retrained to identify other types of brain cancer, said Dr Yu.

Scientists have already designed AI models to profile bowel, lung and breast. Glioblastomas have remained particularly challenging due to their molecular complexity.

Dr. Yu said: "Just like human clinicians who must engage in ongoing education and training, AI tools must keep up with the latest knowledge to remain at peak performance."

CHARM is described in the journal Med.