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...
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| Format: | Article |
| Language: | English |
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Shiraz University of Medical Sciences
2024-04-01
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| Series: | Journal of Biomedical Physics and Engineering |
| Subjects: | |
| Online Access: | https://jbpe.sums.ac.ir/article_49865_99de7777fabfbd1d2d7f07c39cd1010b.pdf |
| _version_ | 1797209469569990656 |
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| author | Mohammad Maskani Samaneh Abbasi Hamidreza Etemad-Rezaee Hamid Abdolahi Amir Zamanpour Alireza Montazerabadi |
| author_facet | Mohammad Maskani Samaneh Abbasi Hamidreza Etemad-Rezaee Hamid Abdolahi Amir Zamanpour Alireza Montazerabadi |
| author_sort | Mohammad Maskani |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T09:55:12Z |
| format | Article |
| id | doaj.art-47536b8ee4ce4f5a8781a9a9e983b548 |
| institution | Directory Open Access Journal |
| issn | 2251-7200 |
| language | English |
| last_indexed | 2024-04-24T09:55:12Z |
| publishDate | 2024-04-01 |
| publisher | Shiraz University of Medical Sciences |
| record_format | Article |
| series | Journal of Biomedical Physics and Engineering |
| spelling | doaj.art-47536b8ee4ce4f5a8781a9a9e983b5482024-04-14T11:11:41ZengShiraz University of Medical SciencesJournal of Biomedical Physics and Engineering2251-72002024-04-0114215115810.31661/jbpe.v0i0.2306-162849865Grading of Gliomas by Contrast-Enhanced CT Radiomics FeaturesMohammad Maskani0Samaneh Abbasi1Hamidreza Etemad-Rezaee2Hamid Abdolahi3Amir Zamanpour4Alireza Montazerabadi5Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, IranDepartment of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, IranDepartment of Neurosurgery, Ghaem Teaching Hospital, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, IranDepartment of Radiologic Sciences, Faculty of Allied Medical Sciences, Kerman University of Medical Sciences, Kerman, IranDepartment of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, IranDepartment of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, IranBackground: 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.https://jbpe.sums.ac.ir/article_49865_99de7777fabfbd1d2d7f07c39cd1010b.pdfradiomicsct scangliomacancerneoplasmstumormachine learning |
| spellingShingle | Mohammad Maskani Samaneh Abbasi Hamidreza Etemad-Rezaee Hamid Abdolahi Amir Zamanpour Alireza Montazerabadi Grading of Gliomas by Contrast-Enhanced CT Radiomics Features Journal of Biomedical Physics and Engineering radiomics ct scan glioma cancer neoplasms tumor machine learning |
| title | Grading of Gliomas by Contrast-Enhanced CT Radiomics Features |
| title_full | Grading of Gliomas by Contrast-Enhanced CT Radiomics Features |
| title_fullStr | Grading of Gliomas by Contrast-Enhanced CT Radiomics Features |
| title_full_unstemmed | Grading of Gliomas by Contrast-Enhanced CT Radiomics Features |
| title_short | Grading of Gliomas by Contrast-Enhanced CT Radiomics Features |
| title_sort | grading of gliomas by contrast enhanced ct radiomics features |
| topic | radiomics ct scan glioma cancer neoplasms tumor machine learning |
| url | https://jbpe.sums.ac.ir/article_49865_99de7777fabfbd1d2d7f07c39cd1010b.pdf |
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