• Todd Charles Hollon, John G Golfinos, Daniel A Orringer, Mitchel Berger, Shawn L Hervey-Jumper, Karin M Muraszko, Christian Freudiger, Jason Heth, Oren Sagher, Cheng Jiang, Asadur Chowdury, Mustafa Nasir Moin, Akhil Kondepudi, Alexander Arash Aabedi, Arjun R Adapa, Wajd Al-Holou, Lisa Wadiura, Georg Widhalm, Volker Neuschmelting, David Reinecke, and Sandra Camelo-Piragua.
• Neurosurgery. 2023 Apr 1; 69 (Suppl 1): 222322-23.

IntroductionMolecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. Access to timely molecular diagnostic testing for brain tumor patients is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment.MethodsBy combining stimulated Raman histology (SRH), a rapid, label-free, non-consumptive, optical imaging method, and deep learning-based image classification, we are able to predict the molecular genetic features used by the World Health Organization (WHO) to define the adult-type diffuse glioma taxonomy, including IDH-1/2, 1p19q-codeletion, and ATRX loss. We developed a multimodal deep neural network training strategy that uses both SRH images and large-scale, public diffuse glioma genomic data (i.e. TCGA, CGGA, etc.) in order to achieve optimal molecular classification performance.ResultsOne institution was used for model training (University of Michigan) and four institutions (NYU, UCSF, Medical University of Vienna, and University Hospital Cologne) were included for patient enrollment in the prospective testing cohort. Using our system, called DeepGlioma, we achieved an average molecular genetic classification accuracy of 93.2% and identified the correct diffuse glioma molecular subgroup with 91.5% accuracy within 2 minutes in the operating room. DeepGlioma outperformed conventional IDH1-R132H immunohistochemistry (94.2% versus 91.4% accuracy) as a first-line molecular diagnostic screening method for diffuse gliomas and can detect canonical and non-canonical IDH mutations.ConclusionsOur results demonstrate how artificial intelligence and optical histology can be used to provide a rapid and scalable alternative to wet lab methods for the molecular diagnosis of brain tumor patients during surgery.Copyright © Congress of Neurological Surgeons 2023. All rights reserved.

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