Evaluating Glioma in Terms of Grading and Predicting IDH-1 Mutation Status by Advanced Diffusion Weighted Imaging： A Comparative Study of DTI， DKI and NODDI
- VernacularTitle:基于不同扩散模型的扩散加权成像在脑胶质瘤分级和预测IDH-1突变的对比分析
- Author:
Ying-qian HUANG
1
;
Jing ZHAO
1
;
Jian-ping CHU
1
;
Yu-liang WANG
2
;
Yi-su TIAN
3
;
Hai-shan QIU
1
;
Zi-huan HUANG
1
Author Information
1. Department of Radiology， The First Affiliated Hospital of Sun Yat-sen University， Guangzhou 510080， China
2. Department of Radiology， Union Shenzhen Hospital of Huazhong University of Science and Technology， Shenzhen 518000， China
3. Department of Radiology， Sichuan Cancer Hospital， Chengdu 610000， China
- Publication Type:Journal Article
- Keywords:
brain;
gliomas;
isocitrate dehydrogenase 1;
magnetic resonance imaging;
molecular subtypes
- From:
Journal of Sun Yat-sen University(Medical Sciences)
2021;42(1):87-94
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo assess the diagnostic efficiency of different diffusion models （DTI， DKI and NODDI） in grading glioma and predicting IDH-1 mutation status， and to further build logistic regression prediction models. MethodsTotally 66 patients （22 females； mean age： 47.8） with pathologically proved gliomas were retrospectively included. All cases underwent bipolar spin echo diffusion examination. Parameters of DKI （MK； Ka； Kr）， DTI （MD and FA） and NODDI （intracellular volume fraction： icvf， orientation dispersion index： odi） were derived. ROIs were manually drawn and corresponding average values were calculated. Logistic regression was performed to build a predictive model. ROC curve was obtained， and Hosmer-lemeshow test was carried out to test the goodness of fit. ResultsDKI， DTI and NODDI parameters were significantly different between HGGs and LGGs （P < 0.01）. And among all diffusion parameters， a further logistic regression model for grading glioma only included age and MK， which showed the highest diagnostic value ［AUC=0.88， AUC 95%CI （0.79， 0.96）］. Hosmer-lemeshow Test present excellent of goodness of fit. With IDH-1 mutation status， NODDI showed no significant value for distinction， whereas DKI and DTI can significantly differentiate IDH-1 mutated and non-mutated glioma （P < 0.05）. Further logistic regression only selected Kr （P <0.01） in the model， which demonstrated the highest diagnostic value ［AUC=0.72， AUC 95%CI （0.59， 0.85）］. ConclusionsDKI is superior to DTI and NODDI in grading gliomas and identifying IDH-1 mutation status. The model of MK value and age variables present the best discriminatory capacity for grading glioma and Kr value may serve as a potential predictive index for identify IDH-1 mutation.
