AUTHOR=Zhou Weiyan , Zhou Zhirui , Wen Jianbo , Xie Fang , Zhu Yuhua , Zhang Zhengwei , Xiao Jianfei , Chen Yijing , Li Ming , Guan Yihui , Hua Tao 

TITLE=A Nomogram Modeling 11C-MET PET/CT and Clinical Features in Glioma Helps Predict IDH Mutation

JOURNAL=Frontiers in Oncology

VOLUME=Volume 10 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.01200

DOI=10.3389/fonc.2020.01200

ISSN=2234-943X

ABSTRACT=Purpose: We developed a 11C-Methionine positron emission tomography/computed tomography (11C-MET PET/CT)-based nomogram model that uses easy-accessible imaging and clinical features to achieve reliable non-invasive isocitrate dehydrogenase (IDH)-mutant prediction with strong clinical translational capability. 
Methods:110 patients with pathologically proven glioma who underwent pretreatment 11C-MET PET/CT were retrospectively reviewed. IDH genotype was determined by IDH1 R132H immunohistochemistry staining. Maximum, mean and peak tumor-to-normal brain tissue(TNRmax, TNRmean, TNRpeak), metabolic tumor volume (MTV), total lesion methionine uptake (TLMU), and standard deviation of SUV(SUVSD) of the lesions on MET PET images were obtained via a dedicated workstation (Siemens. syngo.via). Univariate and multivariate logistic regression models were used to identify the predictive factors for IDH mutation. Nomogram and calibration plots were further performed.
Results: In the entire population, TNRmean, TNRmax, TNRpeak and SUVSD of IDH-mutant glioma patients were significantly lower than these values of IDH wildtype. Receiver operating characteristic (ROC) analysis suggested SUVSD had the best performance for IDH-mutant discrimination (AUC = 0.731, cut-off  0.29, p0.001). All pairs of the 11C-MET PET metrics showed linear associations by Pearson correlation coefficients between 0.228-0.986. Multivariate analyses demonstrated that SUVSD (＞0.29 vs. 0.29 OR: 0.053, p= 0.010), dichotomized brain midline structure involvement(no vs. yes OR: 26.52, p = 0.000) and age (45y vs.＞45y OR:3.23, p = 0.023), were associated with a higher incidence of IDH mutation. The nomogram modeling showed good discrimination, with a C-statistics of 0.866 (95%CI: 0.796 - 0.937) and was well-calibrated. 
Conclusions: 11C-Methionine PET/CT imaging features (SUVSD  and the involvement of brain midline structure) can be conveniently used to facilitate the pre-operative prediction of IDH genotype. The nomogram model based on 11C-Methionine PET/CT and clinical age features might be clinically useful in non-invasive IDH mutation status prediction for untreated glioma patients.