Magnetic resonance imaging-based machine learning radiomics predicts CCND1 expression level and survival in low-grade gliomas

Abstract

Abstract Low-grade glioma (LGG) is associated with increased mortality owing to the recrudescence and tendency for malignant transformation. Therefore, novel prognostic biomarkers must be identified as the current traditional prognostic biomarkers of glioma, including clinicopathological features and imaging examinations, are unable to meet the clinical demand for precision medicine. Accordingly, we aimed to evaluate the prognostic value of cyclin D1 (CCND1) expression levels and construct radiomic models to predict these levels in patients with LGG. A total of 412 LGG cases from The Cancer Genome Atlas (TCGA) were used for gene-based prognostic analysis. Using magnetic resonance imaging (MRI) images stored in The Cancer Imaging Archive with genomic data from TCGA, 149 cases were selected for radiomics feature extraction and model construction. After feature extraction, the radiomic signature was constructed using logistic regression (LR) and support vector machine (SVM) analyses. Involved in the regulation of the cell cycle and immune response, CCND1 was identified as a differentially expressed prognosis-related gene in tumor and normal samples. Landmark analysis revealed that high expression levels of CCND1 were beneficial for survival (P < 0.05) in advanced LGG. Four optimal radiomics features were selected to construct radiomics models. The performance of LR and SVM achieved areas under the curve of 0.703, 0.705, as well as 0.724 and 0.726 in the training and validation sets, respectively. CCND1 expression levels could affect the prognosis of patients with LGG. MRI-based radiomics can serve as a novel tool for predicting the prognosis.