Grading of Gliomas by Contrast-Enhanced CT Radiomics Features

被引：2
作者：
Maskani M. [1 ]
Abbasi S. [1 ]
Etemad-Rezaee H. [2 ]
Abdolahi H. [3 ]
Zamanpour A. [1 ]
Montazerabadi A. [1 ,4 ]
机构：
[1] Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad
[2] Department of Neurosurgery, Ghaem Teaching Hospital, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad
[3] Department of Radiologic Sciences, Faculty of Allied Medical Sciences, Kerman University of Medical Sciences, Kerman
[4] Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad
来源：
Journal of Biomedical Physics and Engineering | 2024年 / 14卷 / 02期
关键词：
Cancer; CT Scan; Glioma; Machine Learning; Neoplasms; Radiomics; Tumor;
D O I：
10.31661/jbpe.v0i0.2306-1628
中图分类号：
学科分类号：
摘要：
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 crossvalidations. 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. © Journal of Biomedical Physics and Engineering.
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页码：151 / 158
页数：7
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