NIMG-69. RAPID INTRAOPERATIVE DIAGNOSIS OF GLIOMA RECURRENCE USING STIMULATED RAMAN HISTOLOGY AND DEEP NEURAL NETWORKS. (11th November 2019)

• Record Type: Journal Article
• Authors: Hollon, Todd; Urias, Esteban; Adapa, Arjun; Jairath, Neil; Save, Akshay; Canoll, Peter D; Lee, Honglak; Freudiger, Christian; Orringer, Daniel
• Is Part Of: Neuro-oncology. Volume 21(2019)Supplement 6
• Page Start: vi177
• Page End: vi177
• Publication Date: 2019-11-11
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noz175.738
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: Accurate intraoperative diagnosis of recurrence versus treatment effect (TE) is essential for determining the management of suspected recurrent gliomas. Cytologic and histoarchitectural changes related to chemoradiation overlap with common findings in recurrent tumors (e.g. atypia, abnormal vasculature, necrosis). Moreover, H&E tissue processing artifact complicates interpretation. Stimulated Raman histology (SRH) uses the intrinsic biochemical properties of fresh, unprocessed surgical specimens to provide rapid label-free digital histologic images. Here, we report an automated technique using deep convolutional neural networks (ConvNet) that differentiates recurrent glioma and TE in fresh surgical specimens imaged using SRH with equivalent accuracy and 10x faster (tissue-to-diagnosis, 2 minutes) than conventional methods. Our ConvNet, based on Google's Inception-ResNet-v2 architecture, was first trained on 3.6 million SRH images from 441 patients with the most common brain tumor subtypes. To optimize the network for classifying glioma recurrence, we used cross-validation (CV) on 35 patients (24 recurrent, 9 TE) for model hyperparameter tuning and to identify an optimal probability threshold to classify recurrence. To perform rigorous model validation, we used a 50 patient external testing set to evaluate overall model accuracy. Over 5 iterations of CV, the mean held-out classification accuracy was 94.8% (range, 91.4 - 97.1%). Using ROC analysis, we found that a probability of recurrence greater than 25% was the optimal threshold to render a recurrence diagnosis for whole-slide SRH images. Using our external testing set, we achieved a classification accuracy of 96% (total 48/50; 30/30 recurrences, 18/20 TE). Moreover, our method effectively identifies regions of glioma recurrence in whole slide SRH at no additional computational cost. Our study demonstrates the feasibility of applying deep learning for intraoperative diagnosis of recurrent gliomas in SRH imaged tissues. In the future, ConvNets may ultimately be used to guide decision-making in the surgical care of recurrent gliomas, independent of conventional neuropathology resources.