NIMG-30. AI-BASED MOLECULAR CLASSIFICATION OF DIFFUSE GLIOMAS USING RAPID, LABEL-FREE OPTICAL HISTOLOGY. (14th November 2022)

• Record Type: Journal Article
• Authors: Hollon, Todd; Jiang, Cheng; Nasir-Moin, Mustafa; Kondepudi, Akhil; Chowdury, Asadur; Al-Holou, Wajd; Castro, Maria; Lowenstein, Pedro; Wadiura, Lisa Irina; Widhalm, Georg; Neuschmelting, Volker; David, Reinecke; von Spreckelsen, Niklas; Berger, Mitchel S; Golfinos, John; Hervey-Jumper, Shawn L; Camelo-Piragua, Sandra; Lee, Honglak; Freudiger, Christian; Orringer, Daniel
• Is Part Of: Neuro-oncology. Volume 24(2022)Supplement 7
• Page Start: vii169
• Page End: vii169
• Publication Date: 2022-11-14
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noac209.648
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: INTRODUCTION: Molecular 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. OBJECTIVE: We aim to develop a rapid (< 90 seconds), AI-based diagnostic screening system that can provide molecular classification of diffuse gliomas and report its use in a prospective, multicenter, international clinical trial of diffuse glioma patients (n = 153). METHODS: By 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 in order to achieve optimal molecular classification performance. RESULTS: One 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 prospective patient enrollment and model testing. 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. DeepGlioma outperformed conventional IDH 1-R132H immunohistochemistry (94.2% versus 91.4% accuracy, respectively) as a first-line molecular diagnostic screening method for diffuse gliomas, detecting canonical and non-canonical IDH mutations with high accuracy. CONCLUSION: Our 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.