Multi-task glioma segmentation and IDH mutation and 1p19q codeletion classification via a deep learning model on multimodal MRI
Objectives: To develop a deep learning model for simultaneous segmentation of glioma lesions and classification of IDH mutation and 1p/19q codeletion status using multimodal MRI. Materials and Methods: We employed a CNN model with Encoder-Decoder architecture for segmentation, followed by fully conn...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-06-01
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| Series: | Meta-Radiology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950162825000207 |
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| author | Erin Beate Bjørkeli Morteza Esmaeili |
| author_facet | Erin Beate Bjørkeli Morteza Esmaeili |
| author_sort | Erin Beate Bjørkeli |
| collection | DOAJ |
| description | Objectives: To develop a deep learning model for simultaneous segmentation of glioma lesions and classification of IDH mutation and 1p/19q codeletion status using multimodal MRI. Materials and Methods: We employed a CNN model with Encoder-Decoder architecture for segmentation, followed by fully connected layers for classification. The model was trained and validated using the BraTS 2020 dataset (132 examinations with known molecular status, split 80/20). Four MRI sequences iamges (T1, T1ce, T2, FLAIR) were used for analysis. Segmentation performance was evaluated using mean Dice Score (mDS) and mean Intersection over Union (mIoU). Classification was assessed using accuracy, sensitivity, and specificity. Results: The model achieved the best segmentation performance with all four modalities (mDS validation = 0.73, mIoU validation = 0.62). Among single modalities, FLAIR performed best (mDS validation = 0.56, mIoU validation = 0.44). For classification, the combined four modalities achieved an overall accuracy of 0.98. However, classification precision for IDH and 1p19q was potentially limited by class imbalance. Conclusion: Our CNN-based Encoder-Decoder model demonstrates the benefit of multimodal MRI for accurate glioma segmentation and shows promising results for molecular subtype classification. Future work will focus on addressing class imbalance and exploring feature integration to enhance classification performance. |
| format | Article |
| id | doaj-art-b2c22dc7793548b08a7a7c9eee1583f3 |
| institution | DOAJ |
| issn | 2950-1628 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Meta-Radiology |
| spelling | doaj-art-b2c22dc7793548b08a7a7c9eee1583f32025-08-20T03:12:38ZengKeAi Communications Co., Ltd.Meta-Radiology2950-16282025-06-013210015210.1016/j.metrad.2025.100152Multi-task glioma segmentation and IDH mutation and 1p19q codeletion classification via a deep learning model on multimodal MRIErin Beate Bjørkeli0Morteza Esmaeili1Department of Diagnostic Imaging, Akershus University Hospital, Lørenskog, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, NorwayDepartment of Diagnostic Imaging, Akershus University Hospital, Lørenskog, Norway; Department of Electrical Engineering and Computer Science, University of Stavanger, Stavanger, Norway; Corresponding author.Objectives: To develop a deep learning model for simultaneous segmentation of glioma lesions and classification of IDH mutation and 1p/19q codeletion status using multimodal MRI. Materials and Methods: We employed a CNN model with Encoder-Decoder architecture for segmentation, followed by fully connected layers for classification. The model was trained and validated using the BraTS 2020 dataset (132 examinations with known molecular status, split 80/20). Four MRI sequences iamges (T1, T1ce, T2, FLAIR) were used for analysis. Segmentation performance was evaluated using mean Dice Score (mDS) and mean Intersection over Union (mIoU). Classification was assessed using accuracy, sensitivity, and specificity. Results: The model achieved the best segmentation performance with all four modalities (mDS validation = 0.73, mIoU validation = 0.62). Among single modalities, FLAIR performed best (mDS validation = 0.56, mIoU validation = 0.44). For classification, the combined four modalities achieved an overall accuracy of 0.98. However, classification precision for IDH and 1p19q was potentially limited by class imbalance. Conclusion: Our CNN-based Encoder-Decoder model demonstrates the benefit of multimodal MRI for accurate glioma segmentation and shows promising results for molecular subtype classification. Future work will focus on addressing class imbalance and exploring feature integration to enhance classification performance.http://www.sciencedirect.com/science/article/pii/S2950162825000207GlioblastomaMolecular subtypeBraTS datasetTCGAIsocitrate dehydrogenase |
| spellingShingle | Erin Beate Bjørkeli Morteza Esmaeili Multi-task glioma segmentation and IDH mutation and 1p19q codeletion classification via a deep learning model on multimodal MRI Meta-Radiology Glioblastoma Molecular subtype BraTS dataset TCGA Isocitrate dehydrogenase |
| title | Multi-task glioma segmentation and IDH mutation and 1p19q codeletion classification via a deep learning model on multimodal MRI |
| title_full | Multi-task glioma segmentation and IDH mutation and 1p19q codeletion classification via a deep learning model on multimodal MRI |
| title_fullStr | Multi-task glioma segmentation and IDH mutation and 1p19q codeletion classification via a deep learning model on multimodal MRI |
| title_full_unstemmed | Multi-task glioma segmentation and IDH mutation and 1p19q codeletion classification via a deep learning model on multimodal MRI |
| title_short | Multi-task glioma segmentation and IDH mutation and 1p19q codeletion classification via a deep learning model on multimodal MRI |
| title_sort | multi task glioma segmentation and idh mutation and 1p19q codeletion classification via a deep learning model on multimodal mri |
| topic | Glioblastoma Molecular subtype BraTS dataset TCGA Isocitrate dehydrogenase |
| url | http://www.sciencedirect.com/science/article/pii/S2950162825000207 |
| work_keys_str_mv | AT erinbeatebjørkeli multitaskgliomasegmentationandidhmutationand1p19qcodeletionclassificationviaadeeplearningmodelonmultimodalmri AT mortezaesmaeili multitaskgliomasegmentationandidhmutationand1p19qcodeletionclassificationviaadeeplearningmodelonmultimodalmri |