IOTG-01. Computational Neurosurgery in Brain Tumors: A paradigm shift on the use of Artificial Intelligence and Connectomics in pre- and intra-operative imaging

Abstract

Abstract Computational Neurosurgery is a novel field where computational modeling and artificial intelligence (AI) are used to analyze diseases of neurosurgical interest. Our aim is to apply AI models to brain tumor (BT) images to a) automatically segment BTs on pre-operative MRI, b) predict the genetic subtype of glioma on intra- and post-operative histological specimens; and c) predict the extent of resection according to connectomics data. For the segmentation task, we used 510 BT images to train a deep learning (DL) model for automatic segmentation of the tumors’ edges and comparison of the AI-generated masks versus experts’ consensus (quantified by means of the dice score). For the histopathology task, we digitalized 266 hematoxylin/eosin slides of gliomas (including 130 IDH-wildtype and 136 IDH-mutant) and applied a DL architecture to predict the IDH genetic status, then validated by immunohistochemistry and genetic sequencing. The datasets were also augmented by generating synthetic glioma images by means of a Generative Adversarial Network methodology. The resection of 10 BTs was also customized according to connectomics data. In the segmentation experiment, we reached a dice score of ~0.9 (out of 1.0), while further demonstrating that only the T1, T1 after gadolinium, and FLAIR sequences are necessary for accurate automatic segmentation. In the histopathology task, we were able to predict the genetic status with accuracy between 76% and 95% using the DL model. The machine learning-based connectome analysis allowed us to perform safe supramaximal resection. We have shown the robustness of applying AI methodology or the automatic segmentation of BTs in MR imaging. Moreover, we have also shown that AI can be used to predict the genetic status, specifically, IDH, in histopathology images of gliomas. Our results support the use of AI in the clinical scenario for a fast and objective computerized characterization of patients affected by BTs.