Grading of Gliomas by Contrast-Enhanced CT Radiomics Features
- سال انتشار: 1403
- محل انتشار: مجله فیزیک و مهندسی پزشکی، دوره: 14، شماره: 2
- کد COI اختصاصی: JR_JBPE-14-2_005
- زبان مقاله: انگلیسی
- تعداد مشاهده: 38
نویسندگان
Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
Department of Neurosurgery, Ghaem Teaching Hospital, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
Department of Radiologic Sciences, Faculty of Allied Medical Sciences, Kerman University of Medical Sciences, Kerman, Iran
Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
چکیده
Background: Gliomas, as Central Nervous System (CNS) tumors, are greatly common with ۸۰% 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 ۶۲ patients (۳۱ with LGG and ۳۱ with HGG). The tumors were segmented by an experienced CT-scan technologist with ۳D slicer software. A total of ۱۴ shape features, ۱۸ histogram-based features, and ۷۵ 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 ۱۳ out of ۱۰۷ 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 ۹۳.۵% accuracy, ۹۶.۷۷% sensitivity, ۹۰.۳% specificity, and ۰.۹۸% AUC in the differentiation of LGGs and HGGs. Conclusion: The proposed method can identify LGG and HGG with ۹۳.۵% accuracy, ۹۶.۷۷% sensitivity, ۹۰.۳% specificity, and ۰.۹۸% AUC, leading to the best treatment for glioma patients by using CT scans based on radiomics analysis.کلیدواژه ها
Radiomics, CT scan, Glioma, cancer, Neoplasms, tumor, Machine Learningاطلاعات بیشتر در مورد COI
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