Joeky T. Senders

TL;DR: A deep learning technique is developed to noninvasively predict IDH genotype in grade II–IV glioma using conventional MR imaging using a multi-institutional data set.

TL;DR: Based on the specific prediction task evaluated and the type of input features included, ML models predicted outcomes after neurosurgery with a median accuracy and area under the receiver operating curve of 94.5% and 0.83, respectively.

TL;DR: It is concluded that ML models have the potential to augment the decision‐making capacity of clinicians in neurosurgical applications; however, significant hurdles remain associated with creating, validating, and deploying ML models in the clinical setting.

TL;DR: A deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hyperintensity and contrast-enhancing tumor, quantitating tumor volumes as well as the product of maximum bidimensional diameters according to the RANO criteria demonstrates potential utility for evaluating tumor burden in complex posttreatment settings.

TL;DR: Across multiple paradigms, ML was found to be a valuable tool for presurgical planning, intraoperative guidance, neurophysiological monitoring, and neurosurgical outcome prediction.