SURG-04. FAST DETECTION OF GLIOMA INFILTRATION USING LABEL-FREE OPTICAL MICROSCOPY AND DEEP NEURAL NETWORKS

Abstract

Abstract INTRODUCTION Fast and accurate detection of tumor infiltration is a central challenge in the comprehensive management of diffuse glioma patients. Despite our best real-time tumor detection strategies, such as intraoperative MRI and fluorescence-guided surgery, dense and safely resectable tumor infiltration remains at the surgical margin in over 85% of diffuse glioma patients. Patterns of treatment failure demonstrate that glioma recurrence occurs at the surgical margin in the majority of patients due to residual tumor burden. Objective Here, we present FastGlioma, an artificial intelligence (AI)-based intraoperative diagnostic system for fast (< 10 seconds) detection of diffuse glioma infiltration at microscopic resolution without the need for tumor-specific markers. Method FastGlioma is trained using large-scale, self-supervised visual feature learning on stimulated Raman histology (SRH), a rapid, label-free, optical microscopy technique, and a GPT-style whole slide SRH tumor infiltration scoring method. We pushed the performance limits of FastGlioma by investigating the trade-off between imaging speed versus accuracy for microscopic tumor detection. RESULTS In a large prospective external testing cohort of diffuse glioma patients (180 patients, 935 surgical margin specimens) who underwent intraoperative SRH imaging, we demonstrate that FastGlioma was able to detect and quantify microscopic tumor infiltration with an average AUROC of 92.1 +/- 0.0%, performing on par with three expert neuropathologists on the same task. FastGlioma is over 10X faster than previous SRH-based methods and 100X faster than conventional H&E histology. Finally, we demonstrate that FastGlioma was > 20% more accurate at detecting dense tumor infiltration when compared to radiologic features or 5-aminolevulinic fluorescence (98.0% versus 77.8% accuracy) for both IDH-mutant diffuse gliomas and IDH-wildtype glioblastomas. CONCLUSION Our results demonstrate how intraoperative optical imaging and deep neural networks can achieve fast and accurate tumor detection, unlocking the role of AI in improving the surgical management of brain tumor patients.