The utility of diffusion MRI with quantitative ADC measurements for differentiating high-grade from low-grade cerebral gliomas: Evidence from a meta-analysis

https://doi.org/10.1016/j.jns.2016.12.008Get rights and content

Highlights

  • •

    Gliomas are the most common primary neoplasms of the brain.

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    Grading of gliomas is important for the determination of appropriate treatment strategies.

  • •

    Preoperative prediction nerve gliomas grades usually can not reliably discriminated using conventional MR techniques.

  • •

    Quantitative ADC values have high accuracy in separating high-grade from low-grade cerebral gliomas.

Abstract

Objective

The aim of this meta-analysis was to predict the grades of cerebral gliomas using quantitative apparent diffusion coefficient (ADC) values.

Materials and methods

A comprehensive search of the PubMed, EMBASE, Web of Science, and Cochrane Library databases was performed up to 8, 2016. The quality assessment of diagnostic accuracy studies (QUADAS 2) was used to evaluate the quality of studies. Statistical analyses included pooling of sensitivity and specificity, positive likelihood ratio (PLR), negative likelihood ratio' (NLR), diagnostic odds ratio (DOR), and diagnostic accuracy values of the included studies using the summary receiver operating characteristic (SROC). All analyses were conducted using STATA (version 12.0), RevMan (version 5.3), and Meta-Disc 1.4 software programs.

Results

Fifteen studies were analyzed and included a total of 821 patients and 821 lesions. In regards to the diagnostic accuracy of ADC maps, the pooled SEN, SPE, PLR, NLR, and DOR with 95%CIs were 0.85(95%CI: 0.80, 0.90)and 0.80 (95%CI: 0.71, 0.87),34.25(95%CI: 2.96, 6.09), 0.18(95%CI:0.13.0.25), and 23.14(95%CI: 14.73, 36.36), respectively. The SROC curve showed an AUC of 0.90. Deeks testing confirmed no significant publication bias in all studies.

Conclusion

Our findings indicate that quantitative ADC values have high accuracy in separating high-grade from low-grade cerebral gliomas. Further studies using a standardized methodology may help guide the use of ADC values for clinical decision-making.

Section snippets

Materials and methods

This systematic review was conducted according to the recommendations of the PRISMA guidelines [12].

Results

A total of 482 studies were identified using the above mentioned search strategy. After screening titles and abstracts, 35 articles were deemed fit for full-text evaluation. The manual search identified no study that met the inclusion criteria. When full-text search was done, 20 articles were omitted because they did not provide enough data to calculate the diagnostic test parameters. Finally, 15 studies [16], [17], [18], [19], [20], [21], [22], [23], [25], [25], [26], [27], [28], [29], [30]

Overall diagnostic accuracy

Sensitivity and specificity were both variable among studies [range, 59.0%–100%] and [range, 59%–95%], respectively. Fig. 4, Fig. 5 depicts the forest plots of sensitivity and specificity for these 15 studies. The summarized sensitivity and specificity of ADC maps for separating high-grade from low-grade gliomas were 0.85[95%CI: 0.80, 0.90] and 0.80 [95%CI: 0.71, 0.87], respectively. The PLR was 4.25[95%CI: 2.96, 6.09]. The NLR was 0.18[95%CI:0.13.0.25]. The DOR was 23.14[95%CI: 14.73, 36.36].

Discussion

Previously, preoperative prediction nerve gliomas grades usually cannot reliably discriminated using conventional MR techniques, such as T2-weighted and gadolinium-enhanced T1-weighted imaging. Since approximately 20% of low-grade gliomas may show partial contrast enhancement and that one third of non-enhancing gliomas are pathologically found to be high grade gliomas. Therefore, new imaging techniques are required [31] [32].

DWI and DTI are new functional MR imaging techniques which could

Acknowledgments

This study was supported by a grant from National Natural Science Foundation of China (No. 81270416 to Xiaoling Zhang).

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    These two authors contributed equally to this work.

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