Researchers from Australian National Universityin Australia, developed a new AI tool (artificial intelligence) to identify and classify brain tumors with up to 95% accuracy. The results of the technology's operation were published in the scientific journal Nature Medicine last Friday (17).
According to Dahn-Tai Hoang, first author of the studygreater accuracy in the diagnosis and categorization of brain tumors is essential for effective patient treatment.
“The current gold standard for identifying different types of brain tumors is profiling based on DNA methylation, which acts as a switch to control genetic activity and which genes are turned on or off,” explains Hoang in press release.
“But the time it takes to carry out this type of test can be a major disadvantage, often requiring several weeks or more, when patients can depend on quick decisions about therapies”, counters the researcher. “There is also a lack of availability of these tests in almost all hospitals in the world”, he adds.
Faced with these challenges, researchers, in collaboration with experts from the National Cancer Institute in the United States, developed the DEPLOY an AI tool capable of predicting DNA methylation and subsequently classify brain tumors into 10 main subtypes .
To do this, DEPLOY relies on microscopic images of a patient's brain tissue, called “histopathological images”. The AI model was trained and validated on large datasets of approximately 4,000 patients from the United States and Europe.
According to Hoang, the tool achieved a accuracy in identifying and classifying brain tumors of 95% . “Additionally, when given a subset of 309 samples that were particularly difficult to classify, DEPLOY was able to provide a more clinically relevant diagnosis than that initially provided by pathologists,” says the researcher.
For the study authors, the tool has the future potential of being a complementary tool for diagnosing brain tumors, adding to the initial work done by a pathologist. Additionally, researchers believe that DEPLOY could eventually be used to help classify other types of cancer.
Source: CNN Brasil
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