Multiple diffusion metrics in differentiating solid glioma from brain inflammation
Background and purposeThe differential diagnosis between solid glioma and brain inflammation is necessary but sometimes difficult. We assessed the effectiveness of multiple diffusion metrics of diffusion-weighted imaging (DWI) in differentiating solid glioma from brain inflammation and compared the...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1320296/full |
| _version_ | 1797339169087815680 |
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| author | Kai Zhao Ankang Gao Eryuan Gao Jinbo Qi Ting Chen Guohua Zhao Gaoyang Zhao Peipei Wang Weijian Wang Jie Bai Yong Zhang Huiting Zhang Guang Yang Xiaoyue Ma Jingliang Cheng |
| author_facet | Kai Zhao Ankang Gao Eryuan Gao Jinbo Qi Ting Chen Guohua Zhao Gaoyang Zhao Peipei Wang Weijian Wang Jie Bai Yong Zhang Huiting Zhang Guang Yang Xiaoyue Ma Jingliang Cheng |
| author_sort | Kai Zhao |
| collection | DOAJ |
| description | Background and purposeThe differential diagnosis between solid glioma and brain inflammation is necessary but sometimes difficult. We assessed the effectiveness of multiple diffusion metrics of diffusion-weighted imaging (DWI) in differentiating solid glioma from brain inflammation and compared the diagnostic performance of different DWI models.Materials and methodsParticipants diagnosed with either glioma or brain inflammation with a solid lesion on MRI were enrolled in this prospective study from May 2016 to April 2023. Diffusion-weighted imaging was performed using a spin-echo echo-planar imaging sequence with five b values (500, 1,000, 1,500, 2000, and 2,500 s/mm2) in 30 directions for each b value, and one b value of 0 was included. The mean values of multiple diffusion metrics based on diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) in the abnormal signal area were calculated. Comparisons between glioma and inflammation were performed. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of diffusion metrics were calculated.Results57 patients (39 patients with glioma and 18 patients with inflammation) were finally included. MAP model, with its metric non-Gaussianity (NG), shows the greatest diagnostic performance (AUC = 0.879) for differentiation of inflammation and glioma with atypical MRI manifestation. The AUC of DKI model, with its metric mean kurtosis (MK) are comparable to NG (AUC = 0.855), followed by NODDI model with intracellular volume fraction (ICVF) (AUC = 0.825). The lowest value was obtained in DTI with mean diffusivity (MD) (AUC = 0.758).ConclusionMultiple diffusion metrics can be used in differentiation of inflammation and solid glioma. Non-Gaussianity (NG) from mean apparent propagator (MAP) model shows the greatest diagnostic performance for differentiation of inflammation and glioma. |
| first_indexed | 2024-03-08T09:42:03Z |
| format | Article |
| id | doaj.art-b204c4183bd04eeabfaeb5a693e1d062 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-08T09:42:03Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-b204c4183bd04eeabfaeb5a693e1d0622024-01-30T04:14:24ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2024-01-011710.3389/fnins.2023.13202961320296Multiple diffusion metrics in differentiating solid glioma from brain inflammationKai Zhao0Ankang Gao1Eryuan Gao2Jinbo Qi3Ting Chen4Guohua Zhao5Gaoyang Zhao6Peipei Wang7Weijian Wang8Jie Bai9Yong Zhang10Huiting Zhang11Guang Yang12Xiaoyue Ma13Jingliang Cheng14Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaMR Research Collaboration, Siemens Healthineers Ltd., Wuhan, ChinaShanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, ChinaDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaBackground and purposeThe differential diagnosis between solid glioma and brain inflammation is necessary but sometimes difficult. We assessed the effectiveness of multiple diffusion metrics of diffusion-weighted imaging (DWI) in differentiating solid glioma from brain inflammation and compared the diagnostic performance of different DWI models.Materials and methodsParticipants diagnosed with either glioma or brain inflammation with a solid lesion on MRI were enrolled in this prospective study from May 2016 to April 2023. Diffusion-weighted imaging was performed using a spin-echo echo-planar imaging sequence with five b values (500, 1,000, 1,500, 2000, and 2,500 s/mm2) in 30 directions for each b value, and one b value of 0 was included. The mean values of multiple diffusion metrics based on diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) in the abnormal signal area were calculated. Comparisons between glioma and inflammation were performed. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of diffusion metrics were calculated.Results57 patients (39 patients with glioma and 18 patients with inflammation) were finally included. MAP model, with its metric non-Gaussianity (NG), shows the greatest diagnostic performance (AUC = 0.879) for differentiation of inflammation and glioma with atypical MRI manifestation. The AUC of DKI model, with its metric mean kurtosis (MK) are comparable to NG (AUC = 0.855), followed by NODDI model with intracellular volume fraction (ICVF) (AUC = 0.825). The lowest value was obtained in DTI with mean diffusivity (MD) (AUC = 0.758).ConclusionMultiple diffusion metrics can be used in differentiation of inflammation and solid glioma. Non-Gaussianity (NG) from mean apparent propagator (MAP) model shows the greatest diagnostic performance for differentiation of inflammation and glioma.https://www.frontiersin.org/articles/10.3389/fnins.2023.1320296/fullmagnetic resonance imagingnon-Gaussiandiffusion-weighted imaginggliomabrain inflammation |
| spellingShingle | Kai Zhao Ankang Gao Eryuan Gao Jinbo Qi Ting Chen Guohua Zhao Gaoyang Zhao Peipei Wang Weijian Wang Jie Bai Yong Zhang Huiting Zhang Guang Yang Xiaoyue Ma Jingliang Cheng Multiple diffusion metrics in differentiating solid glioma from brain inflammation Frontiers in Neuroscience magnetic resonance imaging non-Gaussian diffusion-weighted imaging glioma brain inflammation |
| title | Multiple diffusion metrics in differentiating solid glioma from brain inflammation |
| title_full | Multiple diffusion metrics in differentiating solid glioma from brain inflammation |
| title_fullStr | Multiple diffusion metrics in differentiating solid glioma from brain inflammation |
| title_full_unstemmed | Multiple diffusion metrics in differentiating solid glioma from brain inflammation |
| title_short | Multiple diffusion metrics in differentiating solid glioma from brain inflammation |
| title_sort | multiple diffusion metrics in differentiating solid glioma from brain inflammation |
| topic | magnetic resonance imaging non-Gaussian diffusion-weighted imaging glioma brain inflammation |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1320296/full |
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