Magnetic resonance imaging-based radiomic features for extrapolating infiltration levels of immune cells in lower-grade gliomas.

Abstract

To extrapolate the infiltration levels of immune cells in patients with lower-grade gliomas (LGGs) using magnetic resonance imaging (MRI)-based radiomic features. Aretrospective dataset of 516 patients with LGGs from The Cancer Genome Atlas (TCGA) database was analysed for the infiltration levels of six types of immune cells using Tumor IMmune Estimation Resource (TIMER) based on RNA sequencing data. Radiomic features were extracted from 107 patients whose pre-operative MRI data are available in The Cancer Imaging Archive; 85 and 22 of these patients were assigned to the training and testing cohort, respectively. The least absolute shrinkage and selection operator (LASSO) was applied to select optimal radiomic features to build the radiomic signatures for extrapolating the infiltration levels of immune cells in the training cohort. The developed radiomic signatures were examined in the testing cohort using Pearson's correlation. The infiltration levels of Bcells, CD4+ Tcells, CD8+ Tcells, macrophages, neutrophils and dendritic cells negatively correlated with overall survival in the 516patient cohort when using univariate Cox's regression. Age, Karnofsky Performance Scale, WHO grade, isocitrate dehydrogenase mutant status and the infiltration of neutrophils correlated with survival using multivariate Cox's regression analysis. The infiltration levels of the 6cell types could be estimated by radiomic features in the training cohort, and their corresponding radiomic signatures were built. The infiltration levels of Bcells, CD8+ Tcells, neutrophils and macrophages estimated by radiomics correlated with those estimated by TIMER in the testing cohort. Combining clinical/genomic features with the radiomic signatures only slightly improved the prediction of immune cell infiltrations. We developed MRI-based radiomic models for extrapolating the infiltration levels of immune cells in LGGs. Our results may have implications for treatment planning.