Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features
Gliomas are the most common primary brain tumors. They are classified into 4 grades (Grade I–II-III–IV) according to the guidelines of the World Health Organization (WHO). The accurate grading of gliomas has clinical significance for planning prognostic treatments, pre-diagnosis, monitoring and admi...
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| Format: | Article |
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MDPI AG
2020-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/18/6296 |
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| author | Gökalp Çinarer Bülent Gürsel Emiroğlu Ahmet Haşim Yurttakal |
| author_facet | Gökalp Çinarer Bülent Gürsel Emiroğlu Ahmet Haşim Yurttakal |
| author_sort | Gökalp Çinarer |
| collection | DOAJ |
| description | Gliomas are the most common primary brain tumors. They are classified into 4 grades (Grade I–II-III–IV) according to the guidelines of the World Health Organization (WHO). The accurate grading of gliomas has clinical significance for planning prognostic treatments, pre-diagnosis, monitoring and administration of chemotherapy. The purpose of this study is to develop a deep learning-based classification method using radiomic features of brain tumor glioma grades with deep neural network (DNN). The classifier was combined with the discrete wavelet transform (DWT) the powerful feature extraction tool. This study primarily focuses on the four main aspects of the radiomic workflow, namely tumor segmentation, feature extraction, analysis, and classification. We evaluated data from 121 patients with brain tumors (Grade II, <i>n</i> = 77; Grade III, <i>n</i> = 44) from The Cancer Imaging Archive, and 744 radiomic features were obtained by applying low sub-band and high sub-band 3D wavelet transform filters to the 3D tumor images. Quantitative values were statistically analyzed with MannWhitney U tests and 126 radiomic features with significant statistical properties were selected in eight different wavelet filters. Classification performances of 3D wavelet transform filter groups were measured using accuracy, sensitivity, F1 score, and specificity values using the deep learning classifier model. The proposed model was highly effective in grading gliomas with 96.15% accuracy, 94.12% precision, 100% recall, 96.97% F1 score, and 98.75% Area under the ROC curve. As a result, deep learning and feature selection techniques with wavelet transform filters can be accurately applied using the proposed method in glioma grade classification. |
| first_indexed | 2024-03-10T16:26:39Z |
| format | Article |
| id | doaj.art-96543775ea0d49208d76c9bd470a077a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T16:26:39Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-96543775ea0d49208d76c9bd470a077a2023-11-20T13:12:20ZengMDPI AGApplied Sciences2076-34172020-09-011018629610.3390/app10186296Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic FeaturesGökalp Çinarer0Bülent Gürsel Emiroğlu1Ahmet Haşim Yurttakal2Computer Technologies Department, Yozgat Bozok University, 66100 Yozgat, TurkeyComputer Engineering Department, Kırıkkale University, 71450 Kırıkkale, TurkeyComputer Technologies Department, Yozgat Bozok University, 66100 Yozgat, TurkeyGliomas are the most common primary brain tumors. They are classified into 4 grades (Grade I–II-III–IV) according to the guidelines of the World Health Organization (WHO). The accurate grading of gliomas has clinical significance for planning prognostic treatments, pre-diagnosis, monitoring and administration of chemotherapy. The purpose of this study is to develop a deep learning-based classification method using radiomic features of brain tumor glioma grades with deep neural network (DNN). The classifier was combined with the discrete wavelet transform (DWT) the powerful feature extraction tool. This study primarily focuses on the four main aspects of the radiomic workflow, namely tumor segmentation, feature extraction, analysis, and classification. We evaluated data from 121 patients with brain tumors (Grade II, <i>n</i> = 77; Grade III, <i>n</i> = 44) from The Cancer Imaging Archive, and 744 radiomic features were obtained by applying low sub-band and high sub-band 3D wavelet transform filters to the 3D tumor images. Quantitative values were statistically analyzed with MannWhitney U tests and 126 radiomic features with significant statistical properties were selected in eight different wavelet filters. Classification performances of 3D wavelet transform filter groups were measured using accuracy, sensitivity, F1 score, and specificity values using the deep learning classifier model. The proposed model was highly effective in grading gliomas with 96.15% accuracy, 94.12% precision, 100% recall, 96.97% F1 score, and 98.75% Area under the ROC curve. As a result, deep learning and feature selection techniques with wavelet transform filters can be accurately applied using the proposed method in glioma grade classification.https://www.mdpi.com/2076-3417/10/18/6296deep learningradiomicswaveletgrading |
| spellingShingle | Gökalp Çinarer Bülent Gürsel Emiroğlu Ahmet Haşim Yurttakal Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features Applied Sciences deep learning radiomics wavelet grading |
| title | Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features |
| title_full | Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features |
| title_fullStr | Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features |
| title_full_unstemmed | Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features |
| title_short | Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features |
| title_sort | prediction of glioma grades using deep learning with wavelet radiomic features |
| topic | deep learning radiomics wavelet grading |
| url | https://www.mdpi.com/2076-3417/10/18/6296 |
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