A Non-invasive Radiomic Method Using 18F-FDG PET Predicts Isocitrate Dehydrogenase Genotype and Prognosis in Patients With Glioma - Li, Longfei; Mu, Wei; Wang, Yaning; Liu, Zhenyu; Liu, Zehua; Wang, Yu; Ma, Wenbin; Kong, Ziren; Wang, Shuo; Zhou, Xuezhi; Wei, Wei; Cheng, Xin; et al. (Frontiers in Oncology, 2019)
Purpose: We aimed to analyze 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images by the radiomic method to develop a model and validate the potential value of features reflecting glioma metabolism for predicting isocitrate dehydrogenase (IDH) genotype and prognosis. Methods: PET images of 127 patients were retrospectively analyzed. A series of quantitative features reflecting the metabolic heterogeneity of the tumors were extracted and a radiomic signature was generated using the support vector machine method. Then a combined model that included clinical characteristics and the radiomic signature was constructed by multivariate logistic regression to predict the IDH genotype status, the model was evaluated and verified by receiver operating characteristic (ROC) curves and calibration curves. Finally, Kaplan-Meier curves and log-rank tests were used to analyze the overall survival (OS) according to the predicted result. Results: The generated radiomic signature was significantly associated with IDH genotype (p < 0.05), and could achieve high areas under ROC curve of 0.911 and 0.900 on the training and validation cohorts, respectively, with the incorporation of age and type of tumor metabolism. The good agreement of calibration curves in the validation cohort further validated the efficacy of the constructed model. Moreover, the predicted results showed a significant difference in OS between high- and low-risk groups (p < 0.001). Conclusions: Our results indicate that the 18F-FDG metabolism related features could well predict the IDH genotype of gliomas and stratify the OS of patients with different prognoses.