Deep learning for prediction of isocitrate dehydrogenase mutation in gliomas: a critical approach, systematic review and meta-analysis of the diagnostic test performance using a Bayesian approach

Abstract

This is the first meta-analysis that summarizes the diagnostic performance of DL in predicting <i>IDH</i> mutation status in gliomas via the Bayes theorem. DL algorithms demonstrate excellent diagnostic performance in predicting <i>IDH</i> mutation in gliomas. Radiomic features associated with <i>IDH</i> mutation, and its underlying pathophysiology extracted from advanced MRI may improve prediction probability. However, more studies are required to optimize and increase its reliability. Limitations include obtaining some data via email and lack of training and test sets statistics.