title: Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading.
abstract: Gliomas are the most common intra-axial primary brain tumour; therefore, predicting glioma grade would influence therapeutic strategies. Although several methods based on single or multiple parameters from diagnostic images exist, a definitive method for pre-operatively determining glioma grade remains unknown. We aimed to develop an unsupervised method using multiple parameters from pre-operative diffusion tensor images for obtaining a clustered image that could enable visual grading of gliomas. Fourteen patients with low-grade gliomas and 19 with high-grade gliomas underwent diffusion tensor imaging and three-dimensional T1-weighted magnetic resonance imaging before tumour resection. Seven features including diffusion-weighted imaging, fractional anisotropy, first eigenvalue, second eigenvalue, third eigenvalue, mean diffusivity and raw T2 signal with no diffusion weighting, were extracted as multiple parameters from diffusion tensor imaging. We developed a two-level clustering approach for a self-organizing map followed by the K-means algorithm to enable unsupervised clustering of a large number of input vectors with the seven features for the whole brain. The vectors were grouped by the self-organizing map as protoclusters, which were classified into the smaller number of clusters by K-means to make a voxel-based diffusion tensor-based clustered image. Furthermore, we also determined if the diffusion tensor-based clustered image was really helpful for predicting pre-operative glioma grade in a supervised manner. The ratio of each class in the diffusion tensor-based clustered images was calculated from the regions of interest manually traced on the diffusion tensor imaging space, and the common logarithmic ratio scales were calculated. We then applied support vector machine as a classifier for distinguishing between low- and high-grade gliomas. Consequently, the sensitivity, specificity, accuracy and area under the curve of receiver operating characteristic curves from the 16-class diffusion tensor-based clustered images that showed the best performance for differentiating high- and low-grade gliomas were 0.848, 0.745, 0.804 and 0.912, respectively. Furthermore, the log-ratio value of each class of the 16-class diffusion tensor-based clustered images was compared between low- and high-grade gliomas, and the log-ratio values of classes 14, 15 and 16 in the high-grade gliomas were significantly higher than those in the low-grade gliomas (p < 0.005, p < 0.001 and p < 0.001, respectively). These classes comprised different patterns of the seven diffusion tensor imaging-based parameters. The results suggest that the multiple diffusion tensor imaging-based parameters from the voxel-based diffusion tensor-based clustered images can help differentiate between low- and high-grade gliomas.
authors.affiliation: Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan ; Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan.
authors.forename: Rika
authors.initials: R
authors.lastname: Inano
authors.affiliation: Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan.
authors.forename: Naoya
authors.initials: N
authors.lastname: Oishi
authors.affiliation: Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.
authors.forename: Takeharu
authors.initials: T
authors.lastname: Kunieda
authors.affiliation: Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.
authors.forename: Yoshiki
authors.initials: Y
authors.lastname: Arakawa
authors.affiliation: Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan ; Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan.
authors.forename: Yukihiro
authors.initials: Y
authors.lastname: Yamao
authors.affiliation: Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan ; Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan.
authors.forename: Sumiya
authors.initials: S
authors.lastname: Shibata
authors.affiliation: Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.
authors.forename: Takayuki
authors.initials: T
authors.lastname: Kikuchi
authors.affiliation: Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan.
authors.forename: Hidenao
authors.initials: H
authors.lastname: Fukuyama
authors.affiliation: Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.
authors.forename: Susumu
authors.initials: S
authors.lastname: Miyamoto
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D019540:Area Under Curve; D001932:Brain Neoplasms / Q000473:pathology*; D002648:Child; D016000:Cluster Analysis; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D005910:Glioma / Q000473:pathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading / Q000379:methods*; D012372:ROC Curve; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D055815:Young Adult
keywords: ADC, apparent diffusion coefficient; AUC, area under the curve; BET, FSL's Brain extraction Tool; BLSOM, batch-learning self-organizing map; CI, confidence interval; CNS, central nervous system; DTI, diffusion tensor imaging; DTcI, diffusion tensor-based clustered image; DWI, diffusion-weighted imaging; Diffusion tensor imaging; EPI, echo planar image; FA, fractional anisotropy; FDT, FMRIB's diffusion toolbox; FLAIR, fluid-attenuated inversion-recovery; FSL, FMRIB Software Library; Glioma grading; HGG, high-grade glioma; K-means; KM++, K-means++; KM, K-means; L1, first eigenvalue; L2, second eigenvalue; L3, third eigenvalue; LGG, low-grade glioma; LOOCV, leave-one-out cross-validation; MD, mean diffusivity; MP-RAGE, magnetization-prepared rapid gradient-echo; MRI, magnetic resonance imaging; PET, positron emission tomography; ROC, receiver operating characteristic; ROI, region of interest; S0, raw T2 signal with no diffusion weighting; SOM, self-organizing map; SVM, support vector machine; Self-organizing map; Support vector machine; T1WI, T1-weighted image; T1WIce, contrast-enhanced T1-weighted image; T2WI, T2-weighted image; Voxel-based clustering; WHO, World Health Organization