Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. In this study, we developed DeepGlioma, a rapid (<90 seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas. DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH); a rapid, label-free, non-consumptive, optical imaging method; and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of patients with diffuse glioma (n=153) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular classification accuracy of 93.3±1.6%. Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular screening of patients with diffuse glioma.
@article{arxiv.2303.13610,
title = {Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging},
author = {Todd C. Hollon and Cheng Jiang and Asadur Chowdury and Mustafa Nasir-Moin and Akhil Kondepudi and Alexander Aabedi and Arjun Adapa and Wajd Al-Holou and Jason Heth and Oren Sagher and Pedro Lowenstein and Maria Castro and Lisa Irina Wadiura and Georg Widhalm and Volker Neuschmelting and David Reinecke and Niklas von Spreckelsen and Mitchel S. Berger and Shawn L. Hervey-Jumper and John G. Golfinos and Matija Snuderl and Sandra Camelo-Piragua and Christian Freudiger and Honglak Lee and Daniel A. Orringer},
journal= {arXiv preprint arXiv:2303.13610},
year = {2023}
}