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dc.contributor.author | Takahashi, Yuki | en |
dc.contributor.author | Oishi, Naoya | en |
dc.contributor.author | Yamao, Yukihiro | en |
dc.contributor.author | Kunieda, Takeharu | en |
dc.contributor.author | Kikuchi, Takayuki | en |
dc.contributor.author | Fukuyama, Hidenao | en |
dc.contributor.author | Miyamoto, Susumu | en |
dc.contributor.author | Arakawa, Yoshiki | en |
dc.contributor.alternative | 高橋, 由紀 | ja |
dc.contributor.alternative | 大石, 直也 | ja |
dc.contributor.alternative | 山尾, 幸広 | ja |
dc.contributor.alternative | 菊池, 隆幸 | ja |
dc.contributor.alternative | 福山, 秀直 | ja |
dc.contributor.alternative | 宮本, 享 | ja |
dc.contributor.alternative | 荒川, 芳輝 | ja |
dc.date.accessioned | 2024-03-11T05:09:56Z | - |
dc.date.available | 2024-03-11T05:09:56Z | - |
dc.date.issued | 2023-10 | - |
dc.identifier.uri | http://hdl.handle.net/2433/287292 | - |
dc.description.abstract | [Introduction] Meningiomas are the most common primary central nervous system tumors. Predicting the grade and proliferative activity of meningiomas would influence therapeutic strategies. We aimed to apply the multiple parameters from preoperative diffusion tensor images for predicting meningioma grade and proliferative activity. [Methods] Nineteen patients with low-grade meningiomas and eight with high-grade meningiomas were included. For the prediction of proliferative activity, the patients were divided into two groups: Ki-67 monoclonal antibody labeling index (MIB-1 LI) < 5% (lower MIB-1 LI group; n = 18) and MIB-1 LI ≥ 5% (higher MIB-1 LI group; n = 9). Six features, diffusion-weighted imaging, fractional anisotropy, mean, axial, and radial diffusivities, and raw T2 signal with no diffusion weighting, were extracted as multiple parameters from diffusion tensor imaging. The two-level clustering approach for a self-organizing map followed by the K-means algorithm was applied to cluster a large number of input vectors with the six features. We also validated whether the diffusion tensor-based clustered image (DTcI) was helpful for predicting preoperative meningioma grade or proliferative activity. [Results] The sensitivity, specificity, accuracy, and area under the curve of receiver operating characteristic curves from the 16-class DTcIs for differentiating high- and low-grade meningiomas were 0.870, 0.901, 0.891, and 0.959, and those from the 10-class DTcIs for differentiating higher and lower MIB-1 LIs were 0.508, 0.770, 0.683, and 0.694, respectively. The log-ratio values of class numbers 13, 14, 15, and 16 were significantly higher in high-grade meningiomas than in low-grade meningiomas (p < .001). With regard to MIB-1 LIs, the log-ratio values of class numbers 8, 9, and 10 were higher in meningiomas with higher MIB-1 groups (p < .05). [Conclusion] The multiple diffusion tensor imaging-based parameters from the voxel-based DTcIs can help differentiate between low- and high-grade meningiomas and between lower and higher proliferative activities. | en |
dc.language.iso | eng | - |
dc.publisher | Wiley | en |
dc.rights | © 2023 The Authors. Brain and Behavior published by Wiley Periodicals LLC. | en |
dc.rights | This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. | en |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | diffusion tensor imaging | en |
dc.subject | meningioma | en |
dc.subject | support vector machine | en |
dc.subject | voxel-based clustering | en |
dc.title | Voxel‐based clustered imaging by multiparameter diffusion tensor images for predicting the grade and proliferative activity of meningioma | en |
dc.type | journal article | - |
dc.type.niitype | Journal Article | - |
dc.identifier.jtitle | Brain and Behavior | en |
dc.identifier.volume | 13 | - |
dc.identifier.issue | 10 | - |
dc.relation.doi | 10.1002/brb3.3201 | - |
dc.textversion | publisher | - |
dc.identifier.artnum | e3201 | - |
dc.identifier.pmid | 37644780 | - |
dcterms.accessRights | open access | - |
datacite.awardNumber | 25117008 | - |
datacite.awardNumber | 16H06402 | - |
datacite.awardNumber | 15K09920 | - |
datacite.awardNumber | 18K07712 | - |
datacite.awardNumber | 21K07593 | - |
datacite.awardNumber | 23K08518 | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PLANNED-25117008/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PLANNED-16H06402/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-15K09920/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18K07712/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-21K07593/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-23K08518/ | - |
dc.identifier.eissn | 2162-3279 | - |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.awardTitle | 神経回路の機能的成熟に与るニューロン・グリア相関ダイナミズムの時空間解析 | ja |
jpcoar.awardTitle | モチベーションの脳機能イメージング | ja |
jpcoar.awardTitle | 高速・高精度ノイズ除去技術に基づく脳MRIコネクトームの高精度化 | ja |
jpcoar.awardTitle | 深層学習を用いた高精度ノイズ除去技術の脳画像研究への応用 | ja |
jpcoar.awardTitle | マルチタスク深層学習を用いた脳MRI解析技術の精神・神経疾患への応用 | ja |
jpcoar.awardTitle | 刺激誘発電位解析による側頭葉内ネットワークの解明 | ja |
出現コレクション: | 学術雑誌掲載論文等 |
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