grading of gliomas by contrast-enhanced ct radiomics features

Background: gliomas, as central nervous system (cns) tumors, are greatly common with 80% of malignancy. treatment methods for gliomas, such as surgery, radiation therapy, and chemotherapy depend on the grade, size, location, and the patient’s age.objective: this study aimed to quantify glioma based on the radiomics analysis and classify its grade into high-grade glioma (hgg) or low-grade glioma (lgg) by various machine-learning methods using contrast-enhanced brain computerized tomography (ct) scans. material and methods: this retrospective study involved acquiring and segmenting data, selecting and extracting features, classifying, analyzing, and evaluating classifiers. the study included a total of 62 patients (31 with lgg and 31 with hgg). the tumors were segmented by an experienced ct-scan technologist with 3d slicer software. a total of 14 shape features, 18 histogram-based features, and 75 texture-based features were computed. the area under the curve (auc) and receiver operating characteristic curve (roc) were used to evaluate and compare classification models.results: a total of 13 out of 107 features were selected to differentiate between lggs and hggs and to perform various classifier algorithms with different cross-validations. the best classifier algorithm was linear-discriminant with 93.5% accuracy, 96.77% sensitivity, 90.3% specificity, and 0.98% auc in the differentiation of lggs and hggs. conclusion: the proposed method can identify lgg and hgg with 93.5% accuracy, 96.77% sensitivity, 90.3% specificity, and 0.98% auc, leading to the best treatment for glioma patients by using ct scans based on radiomics analysis.