Radiomics Features Predict CIC Mutation Status in Lower Grade Glioma

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.

Construction of a prognostic immune signature for lower grade glioma that can be recognized by MRI radiomics features to predict survival in LGG patients

“…Previous studies report that MRI radiomics features of gliomas can be used to predict the grade of glioma [25] . And CIC mutations also can be accurately identified by MRI radiomics in LGG patients, as seen in previous studies [76] . Previous papers have focused on the association of MRI radiomics with specific genes/prognosis.…”

Immune Microenvironment Change and Involvement of Circular RNAs in TIL Cells of Recurrent Nasopharyngeal Carcinoma

“…The mechanism of radiation resistance and recurrence has become a major cause of treatment failure in nasopharyngeal carcinoma. However, the mechanisms involved are still very unclear (Suarez et al, 2010;Zhang et al, 2020). Immunity is an important influencing factor in the development of nasopharyngeal carcinoma.…”

Current Advances and Challenges in Radiomics of Brain Tumors

“…suggested a radiomics risk score to alternatively identify the OS in LGGs. When combined with independent clinical prognostic parameters such as WHO grade, age at diagnosis, and seizure, the nomogram based on the risk score exhibited high prognostic accuracy (C-index: training cohort: 0.92, test cohort: 0.70) ( 57 ). They subsequently implemented radiogenomic analysis of high-risk positively associated genes, further revealing the underlying correlated biological processes including hypoxia, angiogenesis, and apoptosis.…”