Abstract
MRI in combination with genomic markers are critical in the management of gliomas. Radiomics and radiogenomics analysis facilitate the quantitative assessment of tumor properties which can be used to model both molecular subtype and predict disease progression. In this work, we report on the Drosophila gene capicua (CIC) mutation biomarker effects alongside radiomics features on the predictive ability of CIC mutation status in lower-grade gliomas (LGG). Genomic data of lower grade glioma (LGG) patients from The Cancer Genome Atlas (TCGA) (n = 509) and corresponding MR images from TCIA (n = 120) were utilized. Following tumor segmentation, radiomics features were extracted from T1, T2, T2 Flair, and T1 contrast enhanced (CE) images. Lasso feature reduction was used to obtain the most important MR image features and then logistic regression used to predict CIC mutation status. In our study, CIC mutation rarely occurred in Astrocytoma but has a high probability of occurrence in Oligodendroglioma. The presence of CIC mutation was found to be associated with better survival of glioma patients (p < 1e-4, HR: 0.2445), even with co-occurrence of IDH mutation and 1p/19q co-deletion (p = 0.0362, HR: 0.3674). An eleven-feature model achieved glioma prediction accuracy of 94.2% (95% CI, 94.03-94.38%), a six-feature model achieved oligodendroglioma prediction accuracy of 92.3% (95% CI, 91.70-92.92%). MR imaging and its derived image of gliomas with CIC mutation appears more complex and non-uniform but are associated with lower malignancy. Our study identified CIC as a potential prognostic factor in glioma which has close associations with survival. MRI radiomic features could predict CIC mutation, and reflect less malignant manifestations such as milder necrosis and larger tumor volume in MRI and its derived images that could help clinical judgment.