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Updated: Jan 7, 2026
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Time-dependent diffusion-weighted imaging assessment of tumor grading and isocitrate dehydrogenase genotypes in
Kiyohisa Kamimura1, Tsubasa Nakano2, Masanori Nakajo2
1Department of Advanced Radiological Imaging, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan. kiyohisa@m2.kufm.kagoshima-u.ac.jp.
Background:
This study aimed to investigate the usefulness of time-dependent diffusion magnetic resonance imaging (MRI) parameters compared with the conventional apparent diffusion coefficient (ADC) in distinguishing tumor grade and isocitrate dehydrogenase (IDH) genotypes of adult-type diffuse gliomas.
Methods:
This retrospective study included 102 patients with adult-type diffuse gliomas. ADC maps obtained using diffusion-weighted imaging at short (7.1 ms) and long (44.5 ms) diffusion times (ADC7.1ms and ADC44.5ms) and maps of ADC changes (cADC) and relative ADC changes (rcADC) between the two diffusion times were generated. The mean, 5th, and 95th percentile values of each parameter were compared between low-grade (LGGs) and high-grade gliomas (HGGs) and between IDH-mutant and IDH-wildtype gliomas. The discriminative performance was assessed using receiver operating characteristic (ROC) analysis, and correlation with Ki-67 labeling index (Ki-67LI) was assessed using Spearman's rank correlation. Multivariable logistic regression analyses were conducted to predict HGGs and IDH-wildtype gliomas.
Results:
In HGGs, the mean and 5th percentile values of ADC44.5ms and ADC7.1ms were significantly lower, whereas cADC and rcADC indices were significantly higher than those in LGGs. Performance of the mean rcADC (area under the ROC curve: 0.925; 95% confidence interval: 0.855-0.967) was significantly better than any index of conventional ADCs for tumor grade classification. The mean rcADC demonstrated the strongest correlation with Ki-67LI (ρ = 0.542, p < 0.0001). Moreover, the 95th percentile of rcADC was an independent predictor of IDH-wildtype gliomas after adjustment for age and sex, was useful for distinguishing IDH-wildtype from IDH-mutant gliomas CONCLUSIONS: The mean rcADC showed the strongest correlation with the Ki-67 LI and achieved better diagnostic performance than conventional PGSE-based ADC for differentiating LGGs from HGGs. In multivariable analyses, the mean and 95th percentile of rcADC were identified as independent predictors of HGGs and IDH-wildtype gliomas, respectively.
