Glioblastoma And Primary Central Nervous System Lymphoma:Preoperative Differentiation By Using MRI-based 3D Texture Analysis

Objectives:To investigate the diagnostic value of magnetic resonance imaging(MRI)-based 3D texture and shape features in the differentiation of glioblastoma(GBM)and primary central nervous system lymphoma(PCNSL).Methods :A total of eighty-two patients,including sixty patients with GBM and twenty-two patients with PCNSL were followed up retrospectively from January 2012 to September 2017.MRI-based 3D texture and shape analysis were performed to evaluate the detectable differences between the two malignancies.The performance of machine-learning models was assessed.The Mann-Whitney U test and receiver operating characteristic(ROC)analysis were performed,and the corresponding sensitivity,specificity,accuracy,and area under the curve(AUC)were calculated.Ultimately,60 GBM patients(33 males,27 females;mean age51.55±13.58 years,range 8-74 years)and 22 PCNSL patients(14males,8 females;mean age 55.18±12.19 years,range 32-78years)were included in this study.All the PCNSLs were of the diffuse large B-cell type,and all patients were immunocompetent.Results:The variables Firstorder_Skewness,Firstorder_Kurtosis,and Ngtdm_Busyness,representing features extracted from contrast-enhanced T1-weighted images,showed high discriminatory power.Firstorder_Skewness was the best selected predictor for classification(AUC=0.86),followed by Ngtdm_Busyness(AUC=0.83)and Firstorder_Kurtosis(AUC=0.80).The sensitivities and specificities ranged from70.0% to 83.3% and from 71.4% to 90.5%,respectively.Among three classification models,the naive Bayes classifier was superior overall,with a high AUC(0.90)and the best specificity(0.91).The support vector machine models provided the best sensitivity and accuracy(0.92 and 0.88,respectively).Conclusions:MRI-based 3D texture analysis has potential utility for preoperative discrimination of GBM and PCNSL.