Papp L, et al. - Authors sought to establish machine-learning–driven survival models for glioma built on in vivo ¹¹C-MET PET characteristics, ex vivo characteristics, and patient characteristics. For this investigation, 70 patients with a treatment-naïve glioma that was ¹¹C-MET–positive and had histopathology-derived ex vivo feature extraction, such as World Health Organization 2007 tumor grade, histology, and isocitrate dehydrogenase 1 R132H mutational status were included. Findings revealed that prediction of survival in amino acid PET–positive glioma patients was highly accurate utilizing computer-supported predictive models based on in vivo, ex vivo, and patient features.