Pooled sensitivity of MK derived from DKI was 91% (confidence interval [CI]: 0.78–0.96), moderate heterogeneity (I 2 = 66.7%, P = .02) is shown in Figure 2. Five studies assessed the specificity of MK derived from DKI, the pooled specificity was 91% (CI: 0.80–0.97). As shown in Figure 2, there was evidence of a considerable heterogeneity (I 2 = 70.8%, P = .01). Overall, the AUC of SROC plot (Fig. 3) was 0.96 (CI: 0.96–0.98), indicating higher diagnostic accuracy for DKI. Besides, DOR (Fig. 4) of DKI also provides strong evidence to illustrate the diagnostic performance. There is no evidence that our research has a threshold effect (Spearman correlation coefficient: 0.300, P = .624).
Sensitivity analysis was carried out and results showed that the statistical difference was not significant in any study excluded. Deek tests for the overall analysis reported publication bias exist in Figure 5 (P = .4).
Factors influencing the diagnostic performance of DKI were performed by Meta-disc1.4. Meta regression analysis showed that country, language and parameter of MRI did not affect the diagnosis of DKI significantly. Meta regression analysis also identified that quality of study had no significant effect on diagnostic performance. No correlation between other investigated covariates and diagnostic performance was identified.
The LGG were characterized by low invasiveness, slow growth, low tumor cell and microvessel density, slow cell proliferation, and insignificant cell atypicality. Therefore, surgery performed without radiotherapy and chemotherapy to achieve long-term survival. However, HGG were characterized by high invasiveness, rapid growth, high density of malignant cells and microvessels, higher cell colonization, and remarkable cell atypicality. Even if patients received adjuvant therapy and chemotherapy after surgery, the prognosis of patients was still poor. Accurate and noninvasive pathologic grading of glioma patients before surgery was crucial to guiding clinicians to select appropriate treatment and improve patient prognosis. In addition, the glioma parenchyma was heterogeneous, and the pathologic and histologic characteristics (tumor cell density, cell perfusion, and microvessel density) of the gliomas would vary from LGG to HGG, which cannot be detected using conventional MR techniques.
In recent years, many studies about diagnostic applications of DKI have been published. The data presented in Li's study showed that MK varied among the different grades of gliomas significantly, MK was significantly lower in the LGG than that in the HGG. This also explained that HGG cell components were more complex than LGG, and it also indicated that the high diagnostic accuracy of DKI in grading gliomas quantitatively. Recently Qi examined the differences in kurtosis parameters between HGG and LGG and they found that The SEN, and SPE of the MK were 88% and 85%, respectively, nevertheless, previous work investigated MK in different glioma grades with the SEN (68%) and SPE (94%). These controversial results were the purpose of our meta-analysis.
We performed a meta-analysis to explore the validity in the utility of DKI for distinguishing HGG from LGG. A pooled analysis demonstrated that the pooled SEN and SPE of MK derived from DKI was 91% (P < .05). The pooled positive LR and negtive LR of MK was 7.58 (CI: 3.15–18.22) and 0.14 (CI: 0.06–0.31). Besides, DOR of DKI also provided strong evidence to illustrate the diagnostic performance, which was consistent with previous studies.[16,17,23]
When a certain numerical limit was exceeded, a fundamental change in diagnostic value occurs, which was interpreted as a threshold effect. Our results indicated that there was no significant relationship between SEN and SPE in the study, revealing no evidence of threshold effect.
Moderate heterogeneity was observed in SEN and SPE, 1 source of heterogeneity was small sample size; however, this was uncontrollable. Sensitivity analysis was used to evaluate the reliability of meta-analysis results by eliminating some low-quality studies or using different statistical methods to explore their effect on the pooled effect. Sensitivity analysis was carried out and results showed that the statistical difference was not significant in any study excluded. Although Deek tests demonstrated that no publication bias in this study, taken into consideration the small sample of this study, we need to be cautious when interpreting the results of this study. Of the 5 articles we included, 4 authors were Chinese and another is Belgium, which may implicitly suggest that diagnostic value elevated result from geographical factor.
Although this was the first meta-analysis to assess the ability of DKI in characterization of gliomas with high SEN and SPE, a few potential limitations of our study should be mentioned: Firstly, the sample size is small; the present literature found only 5 studies that directly distinguished HGG from LGG. Secondly, English and Chinese language restrictions were applied in this analysis, thus to some degree, there exists an inclusion bias. Thirdly, Deek tests for the overall analysis reported publication bias exist in our study. Lastly, parameter values of DKI may be affected by postprocessing techniques, such as the definition of tumor ROI was different in different studies, Jiang was the first to report semi-automatic method applied in glioma measurement. Given this, further a large sample studies were needed, optimization of parameters and postprocessing standardization were helpful to differentiate HGG from LGG accurately.
In brief, this current meta-analysis provides evidence that DKI had the high diagnostic accuracy to differentiate HGG from LGG; however, taken into consideration the small sample of this study, we need to be cautious when interpreting the results of this study.
Data curation: Ruiyu Huang, Yanni Chen.
Formal analysis: Ruiyu Huang, Yanni Chen.
Methodology: Wenfei Li.
Software: Wenfei Li.
Writing – original draft: Ruiyu Huang, Xvfeng Zhang.
Writing – review & editing: Ruiyu Huang, Xvfeng Zhang.
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Keywords:Copyright © 2018 The Authors. Published by Wolters Kluwer Health, Inc. All rights reserved.
diffusion kurtosis; gliomas; grading; magnetic resonance imaging