Machine learning approaches to study glioblastoma: A review of the last decade of applications.

BACKGROUND: Glioblastoma (GB, formally glioblastoma multiforme) is a malignant type of brain cancer that currently has no cure and is characterized by being highly heterogeneous with high rates of re-incidence and therapy resistance. Thus, it is urgent to characterize the mechanisms of GB pathogenesis to help researchers identify novel therapeutic targets to cure this devastating disease. Recently, a promising approach to identifying novel therapeutic targets is the integration of tumor omics data with clinical information using machine learning (ML) techniques. RECENT
FINDINGS: ML has become a valuable addition to the researcher's toolbox, thanks to its flexibility, multidimensional approach, and a growing community of users. The goal of this review is to introduce basic concepts and applications of ML for studying GB to clinicians and practitioners who are new to data science. ML applications include exploring large data sets, finding new relevant patterns, predicting outcomes, or merely understanding associations of the complex molecular networks presented within the tumor. Here, we review ML applications published between 2008 and 2018 and discuss ML strategies intending to identify new potential therapeutic targets to improve the management and treatment of GB.
CONCLUSIONS: ML applications to study GB vary in purpose and complexity, with positive results. In GB studies, ML is often used to analyze high-dimensional datasets with prediction or classification as a primary goal. Despite the strengths of ML techniques, they are not fail-safe and methodological issues can occur in GB studies that use them. This is why researchers need to be aware of these issues when planning and appraising studies that apply ML to the study of GB.