An artificial intelligence (AI) tool has emerged to help doctors combat aggressive brain tumors. Helps identify crucial features that provide guidance during surgery.

The Cryosection Histopathology Review and Evaluation Machine (Charm) is an advanced tool that efficiently analyzes images to identify the genetic profile of gliomas, a type of aggressive brain tumor. Currently, this process takes days or weeks. Kun-Hsing Yu, lead author of a July 7 report in Med, explained that surgeons rely on detailed diagnoses to guide their operations.

Although the tool’s accuracy cannot match current genetic testing, it can quickly predict a tumor’s profile. This rapid analysis allows doctors to proceed with the appropriate treatment without the need to schedule or perform additional surgeries, saving valuable time, Yu explained.

In addition, Charm can distinguish between malignant and benign tumor cells and determine the grade of the tumor, indicating its level of aggressiveness. These are evaluations that human pathologists typically make during surgery. However, according to Yu, Charm could eliminate the need for a 10- to 15-minute wait or the presence of a pathologist on standby during the operation.

Graphic summary of the Charm study. Source: Med

Glioma, particularly the aggressive subtype known as glioblastoma, poses a significant threat, with untreated cases leading to death in less than six months. Tragically, only 17% of people diagnosed with glioblastoma survive beyond the second year, according to the American Association of Neurological Surgeons.

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Yu and his team trained a machine learning algorithm using images of brain surgery samples and validating its accuracy against patient diagnoses. Charm performed better at identifying tumor genetic profiles than other AI systems.

When making critical decisions about the extent of tissue removal and the potential use of drug-coated wafers in the treatment of glioma tumors, surgeons rely heavily on the genetic profile of the tumor. Unfortunately, obtaining this information is a very time consuming process these days.

The research conducted by Yu and his team contributes to a wide range of initiatives that use AI to improve cancer diagnosis and treatment. In a notable editorial published in the June issue of Lancet Oncology, the capabilities of specific systems for their accurate identification of people at elevated risk of pancreatic, lung, and breast cancer were highlighted.

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