Differentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features
Objectives: To develop and validate the model for distinguishing brain abscess from cystic glioma by combining deep transfer learning (DTL) features and hand-crafted radiomics (HCR) features in conventional T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI).Methods: This single-center retrosp...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:2ed773cb17e643c5b45cf997504cd66f2021-11-17T12:08:08ZDifferentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features2296-858X10.3389/fmed.2021.748144https://doaj.org/article/2ed773cb17e643c5b45cf997504cd66f2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmed.2021.748144/fullhttps://doaj.org/toc/2296-858XObjectives: To develop and validate the model for distinguishing brain abscess from cystic glioma by combining deep transfer learning (DTL) features and hand-crafted radiomics (HCR) features in conventional T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI).Methods: This single-center retrospective analysis involved 188 patients with pathologically proven brain abscess (102) or cystic glioma (86). One thousand DTL and 105 HCR features were extracted from the T1WI and T2WI of the patients. Three feature selection methods and four classifiers, such as k-nearest neighbors (KNN), random forest classifier (RFC), logistic regression (LR), and support vector machine (SVM), for distinguishing brain abscess from cystic glioma were compared. The best feature combination and classifier were chosen according to the quantitative metrics including area under the curve (AUC), Youden Index, and accuracy.Results: In most cases, deep learning-based radiomics (DLR) features, i.e., DTL features combined with HCR features, contributed to a higher accuracy than HCR and DTL features alone for distinguishing brain abscesses from cystic gliomas. The AUC values of the model established, based on the DLR features in T2WI, were 0.86 (95% CI: 0.81, 0.91) in the training cohort and 0.85 (95% CI: 0.75, 0.95) in the test cohort, respectively.Conclusions: The model established with the DLR features can distinguish brain abscess from cystic glioma efficiently, providing a useful, inexpensive, convenient, and non-invasive method for differential diagnosis. This is the first time that conventional MRI radiomics is applied to identify these diseases. Also, the combination of HCR and DTL features can lead to get impressive performance.Linlin BoZijian ZhangZekun JiangChao YangPu HuangTingyin ChenYifan WangGang YuXiao TanQuan ChengQuan ChengDengwang LiZhixiong LiuZhixiong LiuFrontiers Media S.A.articlebrain abscessdeep transfer learningradiomicsconvolutional neural networkcystic gliomaMedicine (General)R5-920ENFrontiers in Medicine, Vol 8 (2021) |
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DOAJ |
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EN |
| topic |
brain abscess deep transfer learning radiomics convolutional neural network cystic glioma Medicine (General) R5-920 |
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brain abscess deep transfer learning radiomics convolutional neural network cystic glioma Medicine (General) R5-920 Linlin Bo Zijian Zhang Zekun Jiang Chao Yang Pu Huang Tingyin Chen Yifan Wang Gang Yu Xiao Tan Quan Cheng Quan Cheng Dengwang Li Zhixiong Liu Zhixiong Liu Differentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features |
| description |
Objectives: To develop and validate the model for distinguishing brain abscess from cystic glioma by combining deep transfer learning (DTL) features and hand-crafted radiomics (HCR) features in conventional T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI).Methods: This single-center retrospective analysis involved 188 patients with pathologically proven brain abscess (102) or cystic glioma (86). One thousand DTL and 105 HCR features were extracted from the T1WI and T2WI of the patients. Three feature selection methods and four classifiers, such as k-nearest neighbors (KNN), random forest classifier (RFC), logistic regression (LR), and support vector machine (SVM), for distinguishing brain abscess from cystic glioma were compared. The best feature combination and classifier were chosen according to the quantitative metrics including area under the curve (AUC), Youden Index, and accuracy.Results: In most cases, deep learning-based radiomics (DLR) features, i.e., DTL features combined with HCR features, contributed to a higher accuracy than HCR and DTL features alone for distinguishing brain abscesses from cystic gliomas. The AUC values of the model established, based on the DLR features in T2WI, were 0.86 (95% CI: 0.81, 0.91) in the training cohort and 0.85 (95% CI: 0.75, 0.95) in the test cohort, respectively.Conclusions: The model established with the DLR features can distinguish brain abscess from cystic glioma efficiently, providing a useful, inexpensive, convenient, and non-invasive method for differential diagnosis. This is the first time that conventional MRI radiomics is applied to identify these diseases. Also, the combination of HCR and DTL features can lead to get impressive performance. |
| format |
article |
| author |
Linlin Bo Zijian Zhang Zekun Jiang Chao Yang Pu Huang Tingyin Chen Yifan Wang Gang Yu Xiao Tan Quan Cheng Quan Cheng Dengwang Li Zhixiong Liu Zhixiong Liu |
| author_facet |
Linlin Bo Zijian Zhang Zekun Jiang Chao Yang Pu Huang Tingyin Chen Yifan Wang Gang Yu Xiao Tan Quan Cheng Quan Cheng Dengwang Li Zhixiong Liu Zhixiong Liu |
| author_sort |
Linlin Bo |
| title |
Differentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features |
| title_short |
Differentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features |
| title_full |
Differentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features |
| title_fullStr |
Differentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features |
| title_full_unstemmed |
Differentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features |
| title_sort |
differentiation of brain abscess from cystic glioma using conventional mri based on deep transfer learning features and hand-crafted radiomics features |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/2ed773cb17e643c5b45cf997504cd66f |
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