Glioma grading prediction using multiparametric magnetic resonance imaging-based radiomics combined with proton magnetic resonance spectroscopy and diffusion tensor imaging.

Abstract

To evaluate the efficacy of three-dimensional (3D) segmentation-based radiomics analysis of multiparametric MRI combined with proton magnetic resonance spectroscopy (1 H-MRS) and diffusion tensor imaging (DTI) in glioma grading. A total of 100 patients with histologically confirmed gliomas (grade II-IV) were examined using conventional MRI, 1 H-MRS, and DTI. Tumor segmentations of T1-weighted imaging (T1WI), contrast-enhanced T1WI (T1WI+C), T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) mapping, and fractional anisotropy (FA) mapping were performed. In total, 396 radiomics features were extracted and reduced using basic tests and least absolute shrinkage and selection operator (LASSO) regression. The selected features of each sequence were combined, and logistic regression with ten-fold cross-validation was applied to develop the grading model. Sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were compared. The model developed from the training set was applied to the test set to measure accuracy. One optimal grading quantitative parameter was selected for each 1 H-MRS and DTI analysis. A radiomics nomogram model including radiomics signature, quantitative parameters, and clinical features was developed. T1WI+C exhibited the highest grading efficacy among single sequences (AUC, 0.92; sensitivity, 0.89; specificity, 0.85), but the efficacy of the combined model was higher (AUC, 0.97; sensitivity, 0.94; specificity, 0.91). The AUCs of all models exhibited high accuracy, and no significant differences were observed in AUCs between the training and test sets. The visualized nomogram was developed based on the combined radiomics signature and choline (Cho)/N-acetyl aspartate (NAA) from 1 H-MRS. Multiparametric MRI can be used to predict the pathological grading of high-grade gliomas (HGGs) and low-grade gliomas (LGGs) by combining radiomics features with quantitative parameters. The visualized nomogram may provide an intuitive assessment tool in clinical practice. This trial was not registered, as it was a retrospective study and was approved by the local institutional review board.