Classification of the glioma grading using radiomics analysis
Background Grading of gliomas is critical information related to prognosis and survival. We aimed to apply a radiomics approach using various machine learning classifiers to determine the glioma grading. Methods We considered 285 (high grade n = 210, low grade n = 75) cases obtained from the Brain T...
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| Format: | Article |
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PeerJ Inc.
2018-11-01
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| Online Access: | https://peerj.com/articles/5982.pdf |
| _version_ | 1797424686748925952 |
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| author | Hwan-ho Cho Seung-hak Lee Jonghoon Kim Hyunjin Park |
| author_facet | Hwan-ho Cho Seung-hak Lee Jonghoon Kim Hyunjin Park |
| author_sort | Hwan-ho Cho |
| collection | DOAJ |
| description | Background Grading of gliomas is critical information related to prognosis and survival. We aimed to apply a radiomics approach using various machine learning classifiers to determine the glioma grading. Methods We considered 285 (high grade n = 210, low grade n = 75) cases obtained from the Brain Tumor Segmentation 2017 Challenge. Manual annotations of enhancing tumors, non-enhancing tumors, necrosis, and edema were provided by the database. Each case was multi-modal with T1-weighted, T1-contrast enhanced, T2-weighted, and FLAIR images. A five-fold cross validation was adopted to separate the training and test data. A total of 468 radiomics features were calculated for three types of regions of interest. The minimum redundancy maximum relevance algorithm was used to select features useful for classifying glioma grades in the training cohort. The selected features were used to build three classifier models of logistics, support vector machines, and random forest classifiers. The classification performance of the models was measured in the training cohort using accuracy, sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic curve. The trained classifier models were applied to the test cohort. Results Five significant features were selected for the machine learning classifiers and the three classifiers showed an average AUC of 0.9400 for training cohorts and 0.9030 (logistic regression 0.9010, support vector machine 0.8866, and random forest 0.9213) for test cohorts. Discussion Glioma grading could be accurately determined using machine learning and feature selection techniques in conjunction with a radiomics approach. The results of our study might contribute to high-throughput computer aided diagnosis system for gliomas. |
| first_indexed | 2024-03-09T08:04:26Z |
| format | Article |
| id | doaj.art-6fbb9cb7fbe84f9092d9d900570d4165 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T08:04:26Z |
| publishDate | 2018-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-6fbb9cb7fbe84f9092d9d900570d41652023-12-03T00:23:47ZengPeerJ Inc.PeerJ2167-83592018-11-016e598210.7717/peerj.5982Classification of the glioma grading using radiomics analysisHwan-ho Cho0Seung-hak Lee1Jonghoon Kim2Hyunjin Park3Department of Electronic and Computer Engineering, Sungkyunkwan University, Suwon, KoreaDepartment of Electronic and Computer Engineering, Sungkyunkwan University, Suwon, KoreaDepartment of Electronic and Computer Engineering, Sungkyunkwan University, Suwon, KoreaCenter for Neuroscience Imaging Research, Institute for Basic Science, Suwon, KoreaBackground Grading of gliomas is critical information related to prognosis and survival. We aimed to apply a radiomics approach using various machine learning classifiers to determine the glioma grading. Methods We considered 285 (high grade n = 210, low grade n = 75) cases obtained from the Brain Tumor Segmentation 2017 Challenge. Manual annotations of enhancing tumors, non-enhancing tumors, necrosis, and edema were provided by the database. Each case was multi-modal with T1-weighted, T1-contrast enhanced, T2-weighted, and FLAIR images. A five-fold cross validation was adopted to separate the training and test data. A total of 468 radiomics features were calculated for three types of regions of interest. The minimum redundancy maximum relevance algorithm was used to select features useful for classifying glioma grades in the training cohort. The selected features were used to build three classifier models of logistics, support vector machines, and random forest classifiers. The classification performance of the models was measured in the training cohort using accuracy, sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic curve. The trained classifier models were applied to the test cohort. Results Five significant features were selected for the machine learning classifiers and the three classifiers showed an average AUC of 0.9400 for training cohorts and 0.9030 (logistic regression 0.9010, support vector machine 0.8866, and random forest 0.9213) for test cohorts. Discussion Glioma grading could be accurately determined using machine learning and feature selection techniques in conjunction with a radiomics approach. The results of our study might contribute to high-throughput computer aided diagnosis system for gliomas.https://peerj.com/articles/5982.pdfMachine learningMulti-modal imagingRadiomicsGlioma grading |
| spellingShingle | Hwan-ho Cho Seung-hak Lee Jonghoon Kim Hyunjin Park Classification of the glioma grading using radiomics analysis PeerJ Machine learning Multi-modal imaging Radiomics Glioma grading |
| title | Classification of the glioma grading using radiomics analysis |
| title_full | Classification of the glioma grading using radiomics analysis |
| title_fullStr | Classification of the glioma grading using radiomics analysis |
| title_full_unstemmed | Classification of the glioma grading using radiomics analysis |
| title_short | Classification of the glioma grading using radiomics analysis |
| title_sort | classification of the glioma grading using radiomics analysis |
| topic | Machine learning Multi-modal imaging Radiomics Glioma grading |
| url | https://peerj.com/articles/5982.pdf |
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