Imaging Physics and InformaticsMR diffusion kurtosis imaging for cancer diagnosis: A meta-analysis of the diagnostic accuracy of quantitative kurtosis value and diffusion coefficient
Introduction
Diffusion-weighted imaging (DWI) is a widely used magnetic resonance imaging (MRI) technique type for the detection of cancer from the benign lesions. DWI uses non-contrast enhanced MRI sequence to measure the Brownian motion of water molecules in biological tissues [1]. The diffusivity of water can be quantified by calculating the apparent diffusion coefficient (ADC) values. The relatively high cellularity in malignant tumors commonly leads to a diffusion restriction and thus induces low ADC values yielded [2, 3]. Accordingly, quantitative ADC values could potentially serve as an effective biomarker for non-invasive diagnosis and prognosis of cancer. Unfortunately, some studies indicate there is a similar ADC value between benign and malignant lesions (i.e. idiopathic granulomatous mastitis vs malignant breast lesions [4]; ameloblastomas vs simple bone cysts [5]). This may be attributed to the assumption that the diffusing movement of free water molecules follows Gaussian distribution in the conventional DWI model. In fact, diffusion of water molecules can also be restricted by the presence of various microstructural barriers in biological tissues, such as cellular compartments (intracellular and extracellular spaces) and cell membranes [6], which may make the water molecules diffuse in a non-Gaussian model [7]. Hereby, more advanced diffusion MR imaging techniques that measure diffusional non-Gaussianity are highly required to further assist diagnosis of cancer from the benign lesions.
Diffusion kurtosis imaging (DKI) is an extension of diffusion tensor imaging that assesses the microstructure properties of tissues in a non-Gaussian diffusion-weighted model. From this model, two quantitative parameters can be calculated, including kurtosis values (K, representing deviation from a Gaussian distribution) and diffusion coefficient (D, defining as a corrected ADC for non-Gaussian bias) [7]. Theoretically, DKI with higher K and lower D may exhibit a substantially higher sensitivity than conventional DWI with ADC calculation for cancer detection [8], which has been demonstrated by several studies as following: Jiang et al. demonstrated both K and D had a significantly higher accuracy compared with ADC for the differentiation between benign and malignant sinonasal lesions [sensitivity, 95.70% and 82.60% vs 69.60%; the area under curve (AUC), 0.88 and 0.84 vs. 0.76] [9]. The study of Sun et al. also showed a significantly higher specificity for the differentiation of malignant from benign lesions with the use of K and D than with the use of the ADC (83.00% and 83.00% vs 76.00%) [10]. However, the results of currently published studies on this topic remain controversial. Das et al. found the diagnostic accuracy and AUC of D were not significantly higher in differentiating malignancies from benign pulmonary nodules as compared to ADC (accuracy: 85.70% vs 77.14%; AUC: 0.87 vs 0.81) [11]. Also, Tamura et al. observed no significant difference in AUC between K and ADC for diagnosis of prostate cancer [12]. Therefore, the purpose of this study was to further evaluate the diagnostic performance of quantitative K and D for separating malignant cancer from the benign lesions by performing a systematic meta-analysis, which has not been reported [13].
Section snippets
Search strategy and inclusion criteria
This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [14].
A comprehensive literature search was performed on the PubMed and the Cochrane Library databases to obtain all relevant studies published in English before December of 2017, but without a lower date limit. The following keywords were used: (diffusion kurtosis imaging or diffusion kurtosis) and (cancer or lesions or carcinoma). In addition, all
Characteristics of eligible studies
The results of literature search strategy are illustrated in the PRISMA flowchart (Fig. 1). By searching with the predefined key words, 559 publications were yielded, among which 364 studies were excluded due to duplication. After reviewing the article titles, abstracts and full text, 14 manuscripts ultimately fulfilled the inclusion and exclusion criteria and were selected for data extraction. Table 1 presents the main characteristics of the included studies. These 14 eligible studies were
Discussion
In present study, we, for the first time, used a meta-analysis to investigate the diagnostic accuracy of DKI-derived quantitative parameters (K and D) in the discrimination between malignant cancer and benign lesions. Pooled results indicated both K and D had a good or excellent diagnostic performance in separating malignant cancer from benign lesions, but D may be more superior because it had the higher AUC (0.92 ± 0.02 vs 0.89 ± 0.01) and only its positive LR was >5.0 (6.39, 95%
Funding
This study was supported by National Natural Science Foundation of China (No. 81671679); Shanghai Municipal Science and Technology Development Found (No. 15411952000); Shanghai Shenkang Development Center Found (No. SHDC12014227).
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Acknowledgments
None.
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