Artificial Intelligence Predicts Genetics of Cancerous Brain Tumors in Under 90 Seconds

Using artificial intelligence, researchers have discovered how to screen for genetic mutations in cancerous brain tumors in under 90 seconds and possibly streamline the diagnosis and treatment of gliomas, a study suggests.

In a study of more than 150 patients with diffuse glioma, the newly developed system identified mutations used by the World Health Organization to define molecular subgroups of the condition with a very high accuracy over 90%. 

Molecular classification is increasingly central to the diagnosis and treatment of gliomas, as the benefits and risks of surgery vary among brain tumor patients depending on their genetic makeup. However, access to molecular testing for diffuse glioma is limited and not uniformly available at centers that treat patients with brain tumors. 

“DeepGlioma creates an avenue for accurate and more timely identification that would give providers a better chance to define treatments and predict patient prognosis,” Hollon says.

The system combines deep neural networks with an optical imaging method known as stimulated Raman histology, which was also developed at U-M, to image brain tumor tissue in real time.

“Progress in the treatment of the most deadly brain tumors has been limited in the past decades- in part because it has been hard to identify the patients who would benefit most from targeted therapies,” said senior author Daniel Orringer, M.D. “Rapid methods for molecular classification hold great promise for rethinking clinical trial design and bringing new therapies to patients.” said Orringer. 


Todd Hollon, Cheng Jiang, Asadur Chowdury, Mustafa Nasir-Moin, Akhil Kondepudi, Alexander Aabedi, Arjun Adapa, Wajd Al-Holou, Jason Heth, Oren Sagher, Pedro Lowenstein, Maria Castro, Lisa Irina Wadiura, Georg Widhalm, Volker Neuschmelting, David Reinecke, Niklas von Spreckelsen, Mitchel S. Berger, Shawn L. Hervey-Jumper, John G. Golfinos, Matija Snuderl, Sandra Camelo-Piragua, Christian Freudiger, Honglak Lee, Daniel A. Orringer. Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging. Nature Medicine, 2023; DOI: 10.1038/s41591-023-02252-4

Michigan Medicine – University of Michigan. “Artificial intelligence predicts genetics of cancerous brain tumors in under 90 seconds: Researchers hope it will improve diagnosis and treatment, as well as clinical trial enrollment.” ScienceDaily. ScienceDaily, 23 March 2023. <>.

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Photo by Michael Dziedzic