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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- 11 C-methionine ( 11 C-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 11 C-MET PET characteristics, ex vivo characteristics, and patient characteristics. Methods: The study included 70 patients with a treatment-naïve glioma that was 11 C-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 11 C-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 (M36 IEP ); another based on in vivo and patient information only (M36 IP ); and a third based on in vivo information only (M36 I ). In addition, a binning-independent model based on ex vivo and patient information only (M36 EP ) 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 M36 IEP , 0.87 for M36 EP , 0.77 for M36 IP , and 0.72 for M36 I  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.

the journal of nuclear medicine

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

Glioma Survival Prediction with Combined Analysis of In Vivo 11C-MET PET Features, Ex Vivo Features, and Patient Features by Supervised Machine Learning

Glioma Survival Prediction with Combined Analysis of In Vivo ¹¹C-MET PET Features, Ex Vivo Features, and Patient Features by Supervised Machine Learning