Quantitative analysis of neurite orientation dispersion and density imaging in grading gliomas and detecting IDH-1 gene mutation status.

Autor: Zhao J; Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China., Li JB; Department of Clinical Research, Sun Yat-sen University Cancer Center, 651, Dong Feng Dong Lu Road, Guangzhou, Guangdong 510060, China., Wang JY; Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China., Wang YL; Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China., Liu DW; Department of Pathology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China., Li XB; Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China., Song YK; Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China., Tian YS; Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China., Yan X; MR Collaboration NE Asia, Siemens Healthcare 278, Zhou Zhu Road, Nanhui, Shanghai 201318, China., Li ZH; Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China., He SF; Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China., Huang XL; Department of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 33, Ying Feng Lu Road, Hai Zhu district, Guangzhou, Guangdong 510235, China., Jiang L; Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China., Yang ZY; Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China., Chu JP; Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China. Electronic address: chujping@mail.sysu.edu.cn.
Jazyk: angličtina
Zdroj: NeuroImage. Clinical [Neuroimage Clin] 2018 Apr 12; Vol. 19, pp. 174-181. Date of Electronic Publication: 2018 Apr 12 (Print Publication: 2018).
DOI: 10.1016/j.nicl.2018.04.011
Abstrakt: Background and Purpose: Neurite orientation dispersion and density imaging (NODDI) is a new diffusion MRI technique that has rarely been applied for glioma grading. The purpose of this study was to quantitatively evaluate the diagnostic efficiency of NODDI in tumour parenchyma (TP) and peritumoural area (PT) for grading gliomas and detecting isocitrate dehydrogenase-1 ( IDH-1 ) mutation status.
Methods: Forty-two patients (male: 23, female: 19, mean age: 44.5 y) were recruited and underwent whole brain NODDI examination. Intracellular volume fraction (icvf) and orientation dispersion index (ODI) maps were derived. Three ROIs were manually placed on TP and PT regions for each case. The corresponding average values of icvf and ODI were calculated, and their diagnostic efficiency was assessed.
Results: Tumours with high icvf TP (≥0.306) and low icvf PT (≤0.331) were more likely to be high-grade gliomas (HGGs), while lesions with low icvf TP (<0.306) and high icvf PT (>0.331) were prone to be low-grade gliomas (LGGs) ( P  < 0.001). A multivariate logistic regression model including patient age and icvf values in TP and PT regions most accurately predicted glioma grade (AUC = 0.92, P  < 0.001), with a sensitivity and specificity of 92% and 89%, respectively. However, no significant differences were found in NODDI metrics for differentiating IDH-1 mutation status.
Conclusions: The quantitative NODDI metrics in the TP and PT regions are highly valuable for glioma grading. A multivariate logistic regression model using the patient age and the icvf values in TP and PT regions showed very high predictive power. However, the utility of NODDI metrics for detecting IDH-1 mutation status has not been fully explored, as a larger sample size may be necessary to uncover benefits.
Databáze: MEDLINE
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