PT - JOURNAL ARTICLE AU - László Papp AU - Nina Pötsch AU - Marko Grahovac AU - Victor Schmidbauer AU - Adelheid Woehrer AU - Matthias Preusser AU - Markus Mitterhauser AU - Barbara Kiesel AU - Wolfgang Wadsak AU - Thomas Beyer AU - Marcus Hacker AU - Tatjana Traub-Weidinger TI - Glioma Survival Prediction with Combined Analysis of In Vivo <sup>11</sup>C-MET PET Features, Ex Vivo Features, and Patient Features by Supervised Machine Learning AID - 10.2967/jnumed.117.202267 DP - 2018 Jun 01 TA - Journal of Nuclear Medicine PG - 892--899 VI - 59 IP - 6 4099 - http://jnm.snmjournals.org/content/59/6/892.short 4100 - http://jnm.snmjournals.org/content/59/6/892.full SO - J Nucl Med2018 Jun 01; 59 AB - Gliomas are the most common type of tumor in the brain. Although the definite diagnosis is routinely made ex vivo by histopathologic and molecular examination, diagnostic work-up of patients with suspected glioma is mainly done using MRI. Nevertheless, l-S-methyl-11C-methionine (11C-MET) PET holds great potential in the characterization of gliomas. The aim of this study was to establish machine-learning–driven survival models for glioma built on in vivo 11C-MET PET characteristics, ex vivo characteristics, and patient characteristics. Methods: The study included 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. The 11C-MET–positive primary tumors were delineated semiautomatically on PET images, followed by the extraction of tumor-to-background–based general and higher-order textural features by applying 5 different binning approaches. In vivo and ex vivo features, as well as patient characteristics (age, weight, height, body mass index, Karnofsky score), were merged to characterize the tumors. Machine-learning approaches were used to identify relevant in vivo, ex vivo, and patient features and their relative weights for predicting 36-mo survival. The resulting feature weights were used to establish 3 predictive models per binning configuration: one model based on a combination of in vivo, ex vivo, and clinical patient information (M36IEP); another based on in vivo and patient information only (M36IP); and a third based on in vivo information only (M36I). In addition, a binning-independent model based on ex vivo and patient information only (M36EP) was created. The established models were validated in a Monte Carlo cross-validation scheme. Results: The most prominent machine-learning–selected and –weighted features were patient-based and ex vivo–based, followed by in vivo–based. The highest areas under the curve for our models as revealed by the Monte Carlo cross-validation were 0.9 for M36IEP, 0.87 for M36EP, 0.77 for M36IP, and 0.72 for M36I. Conclusion: Prediction of survival in amino acid PET–positive glioma patients was highly accurate using computer-supported predictive models based on in vivo, ex vivo, and patient features.