Abstrait

Artificial Intelligence: New Applications in Neuro-Oncology

Evans John

Artificial intelligence (AI) techniques have the potential to increase the accuracy of medical diagnostic and treatment procedures due to the exponential rise of computer algorithms. The spearhead of this revolution has been and most likely will remain the field of radiomics in neuro-oncology. Different AI techniques applied to conventional and advanced neuro-oncology MRI data can already distinguish between pseudo progression and real progression, delineate infiltrating margins of diffuse gliomas, and predict recurrence and survival more accurately than techniques currently used in clinical practise. By enabling non-invasive molecular environment sampling with great spatial resolution and offering a systems-level insight of underlying diverse cellular and molecular processes, radio genomics will also increase our understanding of cancer biology. These AI-based radiomic and radio genomic tools have the potential to stratify patients into more accurate initial diagnostic and therapeutic pathways and enable better dynamic treatment monitoring in this era of personalised medicine by providing in vivo markers of spatial and molecular heterogeneity. Although there are still many obstacles to overcome, as AI technology is developed and approved for clinical use, radiologic practise is expected to undergo significant change.