Evidence map›Paper›PMID 37644780›Full record

ArticleBrain and behavior2023

Voxel-based clustered imaging by multiparameter diffusion tensor images for predicting the grade and proliferative activity of meningioma.

Yuki Takahashi, Naoya Oishi, Yukihiro Yamao, Takeharu Kunieda, Takayuki Kikuchi, Hidenao Fukuyama, Susumu Miyamoto, Yoshiki Arakawa

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Article in Brain and behavior, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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2 citing papers in PubMed.

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8 authors.

Yuki TakahashiDepartment of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Naoya OishiDepartment of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Yukihiro YamaoDepartment of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.ORCID 0000-0002-9615-2353
Takeharu KuniedaDepartment of Neurosurgery, Ehime University Graduate School of Medicine, Toon, Japan.
Takayuki KikuchiDepartment of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Hidenao FukuyamaYasu City Hospital, Yasu, Japan.
Susumu MiyamotoDepartment of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Yoshiki ArakawaDepartment of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.

Funding

Japan Society for the Promotion of Science 15K09920Japan Society for the Promotion of Science 18K07712Japan Society for the Promotion of Science 21K07593Japan Society for the Promotion of Science 23K08518Scientific Research on Innovative Areas 16H06402Scientific Research on Innovative Areas 25117008
6 · The paper itself

Abstract

introductionMeningiomas 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.

methodsNineteen 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.

resultsThe 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).

conclusionThe 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.

Indexed as

Diffusion Tensor ImagingMeningeal NeoplasmsMeningiomaAdultAgedCell ProliferationDiffusion Magnetic Resonance ImagingFemaleHumansKi-67 AntigenMaleMiddle AgedNeoplasm GradingSensitivity and SpecificityKi-67 Antigendiffusion tensor imagingmeningiomasupport vector machinevoxel-based clustering

Identifiers

PMID37644780
PMCPMC10570481

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