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Multimodal MRI Image Decision Fusion-Based Network for Glioma Classification
PURPOSE: Glioma is the most common primary brain tumor, with varying degrees of aggressiveness and prognosis. Accurate glioma classification is very important for treatment planning and prognosis prediction. The main purpose of this study is to design a novel effective algorithm for further improvin...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8907622/ https://www.ncbi.nlm.nih.gov/pubmed/35280828 http://dx.doi.org/10.3389/fonc.2022.819673 |
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author | Guo, Shunchao Wang, Lihui Chen, Qijian Wang, Li Zhang, Jian Zhu, Yuemin |
author_facet | Guo, Shunchao Wang, Lihui Chen, Qijian Wang, Li Zhang, Jian Zhu, Yuemin |
author_sort | Guo, Shunchao |
collection | PubMed |
description | PURPOSE: Glioma is the most common primary brain tumor, with varying degrees of aggressiveness and prognosis. Accurate glioma classification is very important for treatment planning and prognosis prediction. The main purpose of this study is to design a novel effective algorithm for further improving the performance of glioma subtype classification using multimodal MRI images. METHOD: MRI images of four modalities for 221 glioma patients were collected from Computational Precision Medicine: Radiology-Pathology 2020 challenge, including T1, T2, T1ce, and fluid-attenuated inversion recovery (FLAIR) MRI images, to classify astrocytoma, oligodendroglioma, and glioblastoma. We proposed a multimodal MRI image decision fusion-based network for improving the glioma classification accuracy. First, the MRI images of each modality were input into a pre-trained tumor segmentation model to delineate the regions of tumor lesions. Then, the whole tumor regions were centrally clipped from original MRI images followed by max–min normalization. Subsequently, a deep learning-based network was designed based on a unified DenseNet structure, which extracts features through a series of dense blocks. After that, two fully connected layers were used to map the features into three glioma subtypes. During the training stage, we used the images of each modality after tumor segmentation to train the network to obtain its best accuracy on our testing set. During the inferring stage, a linear weighted module based on a decision fusion strategy was applied to assemble the predicted probabilities of the pre-trained models obtained in the training stage. Finally, the performance of our method was evaluated in terms of accuracy, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), etc. RESULTS: The proposed method achieved an accuracy of 0.878, an AUC of 0.902, a sensitivity of 0.772, a specificity of 0.930, a PPV of 0.862, an NPV of 0.949, and a Cohen’s Kappa of 0.773, which showed a significantly higher performance than existing state-of-the-art methods. CONCLUSION: Compared with current studies, this study demonstrated the effectiveness and superiority in the overall performance of our proposed multimodal MRI image decision fusion-based network method for glioma subtype classification, which would be of enormous potential value in clinical practice. |
format | Online Article Text |
id | pubmed-8907622 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89076222022-03-11 Multimodal MRI Image Decision Fusion-Based Network for Glioma Classification Guo, Shunchao Wang, Lihui Chen, Qijian Wang, Li Zhang, Jian Zhu, Yuemin Front Oncol Oncology PURPOSE: Glioma is the most common primary brain tumor, with varying degrees of aggressiveness and prognosis. Accurate glioma classification is very important for treatment planning and prognosis prediction. The main purpose of this study is to design a novel effective algorithm for further improving the performance of glioma subtype classification using multimodal MRI images. METHOD: MRI images of four modalities for 221 glioma patients were collected from Computational Precision Medicine: Radiology-Pathology 2020 challenge, including T1, T2, T1ce, and fluid-attenuated inversion recovery (FLAIR) MRI images, to classify astrocytoma, oligodendroglioma, and glioblastoma. We proposed a multimodal MRI image decision fusion-based network for improving the glioma classification accuracy. First, the MRI images of each modality were input into a pre-trained tumor segmentation model to delineate the regions of tumor lesions. Then, the whole tumor regions were centrally clipped from original MRI images followed by max–min normalization. Subsequently, a deep learning-based network was designed based on a unified DenseNet structure, which extracts features through a series of dense blocks. After that, two fully connected layers were used to map the features into three glioma subtypes. During the training stage, we used the images of each modality after tumor segmentation to train the network to obtain its best accuracy on our testing set. During the inferring stage, a linear weighted module based on a decision fusion strategy was applied to assemble the predicted probabilities of the pre-trained models obtained in the training stage. Finally, the performance of our method was evaluated in terms of accuracy, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), etc. RESULTS: The proposed method achieved an accuracy of 0.878, an AUC of 0.902, a sensitivity of 0.772, a specificity of 0.930, a PPV of 0.862, an NPV of 0.949, and a Cohen’s Kappa of 0.773, which showed a significantly higher performance than existing state-of-the-art methods. CONCLUSION: Compared with current studies, this study demonstrated the effectiveness and superiority in the overall performance of our proposed multimodal MRI image decision fusion-based network method for glioma subtype classification, which would be of enormous potential value in clinical practice. Frontiers Media S.A. 2022-02-24 /pmc/articles/PMC8907622/ /pubmed/35280828 http://dx.doi.org/10.3389/fonc.2022.819673 Text en Copyright © 2022 Guo, Wang, Chen, Wang, Zhang and Zhu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Guo, Shunchao Wang, Lihui Chen, Qijian Wang, Li Zhang, Jian Zhu, Yuemin Multimodal MRI Image Decision Fusion-Based Network for Glioma Classification |
title | Multimodal MRI Image Decision Fusion-Based Network for Glioma Classification |
title_full | Multimodal MRI Image Decision Fusion-Based Network for Glioma Classification |
title_fullStr | Multimodal MRI Image Decision Fusion-Based Network for Glioma Classification |
title_full_unstemmed | Multimodal MRI Image Decision Fusion-Based Network for Glioma Classification |
title_short | Multimodal MRI Image Decision Fusion-Based Network for Glioma Classification |
title_sort | multimodal mri image decision fusion-based network for glioma classification |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8907622/ https://www.ncbi.nlm.nih.gov/pubmed/35280828 http://dx.doi.org/10.3389/fonc.2022.819673 |
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