Artificial intelligence in neuro-oncology

Abstract

Artificial intelligence (AI) describes the application of computer algorithms to the solution of problems that have traditionally required human intelligence. Although formal work in AI has been slowly advancing for almost 70 years, developments in the last decade, and particularly in the last year, have led to an explosion of AI applications in multiple fields. Neuro-oncology has not escaped this trend. Given the expected integration of AI-based methods to neuro-oncology practice over the coming years, we set to provide an overview of existing technologies as they are applied to the neuropathology and neuroradiology of brain tumors. We highlight current benefits and limitations of these technologies and offer recommendations on how to appraise novel AI-tools as they undergo consideration for integration into clinical workflows.

Practical Application of Deep Learning in Diagnostic Neuropathology—Reimagining a Histological Asset in the Era of Precision Medicine

“…An example of attention weights and Grad-CAM activation maps can be seen in Figure 2. Variation in the manual labeling of histopathologic images in the training of DL models presents a further challenge, due to interobserver variability in the evaluation of histopathologic images by pathologists [59]. For instance, there is a low concordance rate between pathologists in evaluating mitotic count, which remains a key determinant of histopathologic grade in various tumor entities, including, but not limited to, various gliomas and meningioma [62,63].…”

“…Model interpretability poses another meaningful barrier to implementation and efforts should be directed towards the development of explainable models for use by clinical practitioners [ 59 ]. Deep learning models can be difficult to interpret, given that feature selection is built into the training process.…”