Radiogenomics correlation between MR imaging features and mRNA-based subtypes in lower-grade glioma

Abstract

Abstract Background To investigate associations between lower-grade glioma (LGG) mRNA-based subtypes (R1-R4) and MR features. Methods mRNA-based subtyping was obtained from the LGG dataset in The Cancer Genome Atlas (TCGA). We identified matching patients (n = 145) in The Cancer Imaging Archive (TCIA) who underwent MR imaging. The associations between mRNA-based subtypes and MR features were assessed. Results In the TCGA-LGG dataset, patients with the R2 subtype had the shortest median OS months (P 5%, OR: 14.733; P 5%, OR: 0.14; P < 0.001) was negatively associated with the R4 subtype (AUC: 0.672). The average accuracy of the ten-fold cross validation was 71%. For the prediction of the R2 subtype, the nomogram showed good discrimination and calibration. Decision curve analysis demonstrated that prediction with the R2 model was clinically useful. Conclusions Patients with the R2 subtype had the worst prognosis. We demonstrated that MRI features can identify distinct LGG mRNA-based molecular subtypes.

Keywords

• Logistic regression
• Lower-grade gliomas
• mRNA-based subtypes
• MR biomarker
• Time-dependent ROC