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An instrument based on artificial intelligence (AI) has emerged to aid physicians in the treatment of aggressive brain tumors. It AIDS in the identification of important characteristics that guide surgery.
The Cryosection Histopathology Assessment and Review Machine (CHARM) is an advanced tool that efficiently analyzes images to determine the genetic profile of gliomas, a form of aggressive brain tumor.
Currently, this procedure requires several days. According to Kun-Hsing Yu, principal author of a report published in Med on July 7, surgeons rely on precise diagnoses to guide their procedures.
Although the tool's precision may not match that of current genetic tests, it can forecast a tumor's profile quickly. According to Yu, this quick analysis allows physicians to proceed with appropriate treatment without the need to schedule and perform additional surgeries, saving valuable time.
In addition, CHARM can differentiate between malignant and benign tumor cells and determine the tumor's grade, which indicates the level of its aggressiveness.
Graphic abstract of the Charm study. Source: Med
Typically, human pathologists conduct these assessments during surgical procedures. According to Yu, however, CHARM could eliminate the need for a 10- to 15-minute delay or the presence of a pathologist on standby during the operation.
Glioma, especially the aggressive subtype known as glioblastoma, poses a significant threat, as untreated cases result in mortality in less than six months. According to the American Association of Neurological Surgeons, only 17 per cent of glioblastoma patients survive beyond the second year.
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Yu and his colleagues trained a machine-learning algorithm using images of brain surgery samples and validated its accuracy by comparing it to patient diagnoses. Compared to other ai systems , CHARM performed significantly better at identifying tumor genetic profiles.
Surgeons significantly rely on the genetic profile of glioma tumors when making crucial decisions regarding the extent of tissue removal and the potential use of drug-coated wafers to treat glioma tumors. Obtaining this information at the moment is a time-consuming process.
The research conducted by Yu and his team contributes to a wide variety of initiatives that employ AI to improve the diagnosis and treatment of cancer .
In a notable editorial published in the June issue of the lancet oncology , the capabilities of specific systems for accurately identifying individuals with an increased risk of pancreatic, lung, and breast cancer were highlighted.