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Maskani, Mohammad, Abbasi, Samaneh, Etemad-Rezaee, Hamidreza, Abdolahi, Hamid, Zamanpour, Amir, Montazerabadi, Alireza. (1403). Grading of Gliomas by Contrast-Enhanced CT Radiomics Features. سامانه مدیریت نشریات علمی, 14(2), 151-158. doi: 10.31661/jbpe.v0i0.2306-1628
Mohammad Maskani; Samaneh Abbasi; Hamidreza Etemad-Rezaee; Hamid Abdolahi; Amir Zamanpour; Alireza Montazerabadi. "Grading of Gliomas by Contrast-Enhanced CT Radiomics Features". سامانه مدیریت نشریات علمی, 14, 2, 1403, 151-158. doi: 10.31661/jbpe.v0i0.2306-1628
Maskani, Mohammad, Abbasi, Samaneh, Etemad-Rezaee, Hamidreza, Abdolahi, Hamid, Zamanpour, Amir, Montazerabadi, Alireza. (1403). 'Grading of Gliomas by Contrast-Enhanced CT Radiomics Features', سامانه مدیریت نشریات علمی, 14(2), pp. 151-158. doi: 10.31661/jbpe.v0i0.2306-1628
Maskani, Mohammad, Abbasi, Samaneh, Etemad-Rezaee, Hamidreza, Abdolahi, Hamid, Zamanpour, Amir, Montazerabadi, Alireza. Grading of Gliomas by Contrast-Enhanced CT Radiomics Features. سامانه مدیریت نشریات علمی, 1403; 14(2): 151-158. doi: 10.31661/jbpe.v0i0.2306-1628

Grading of Gliomas by Contrast-Enhanced CT Radiomics Features

Journal of Biomedical Physics and Engineering
دوره 14، شماره 2، تیر 2024، صفحه 151-158    اصل مقاله (821.91 K)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2306-1628
نویسندگان
Mohammad Maskani1؛ Samaneh Abbasi1؛ Hamidreza Etemad-Rezaee2؛ Hamid Abdolahi3؛ Amir Zamanpour1؛ Alireza Montazerabadi* 1، 4
1Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
2Department of Neurosurgery, Ghaem Teaching Hospital, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
3Department of Radiologic Sciences, Faculty of Allied Medical Sciences, Kerman University of Medical Sciences, Kerman, Iran
4Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
چکیده
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.

تازه های تحقیق

Alireza Montazerabadi (Google Scholar)

کلیدواژه‌ها
Radiomics؛ CT Scan؛ Glioma؛ Cancer؛ Neoplasms؛ Tumor؛ Machine Learning
سایر فایل های مرتبط با مقاله
مراجع
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