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

Abstract

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.

Keywords

• diffusion tensor imaging
• meningioma
• support vector machine
• voxel‐based clustering