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A Systematic Review and Meta-analysis of the Accuracy of Diffusion-weighted MRI in the Detection of Malignant Pulmonary Nodules and Masses

Rationale and Objectives

To perform a meta-analysis to assess the diagnostic performance of the diffusion-weighted magnetic resonance imaging (DWI) technique in discrimination of benign and malignant pulmonary nodules or masses.

Materials and Methods

Data sources were studies published in PubMed, MEDLINE, EMBASE, Cochrane Library, and China National Knowledge Infrastructure databases from January 2001 to May 2013. Studies evaluating the diagnostic accuracy of DWI for benign/malignant discrimination of pulmonary nodules in English or Chinese language were considered for inclusion. Methodological quality was assessed by the quality assessment of diagnostic studies instrument. Sensitivities, specificities, predictive values, diagnostic odds ratios (DORs), and areas under the receiver operating characteristic curve (AUCs) were calculated. Potential threshold effect, heterogeneity, and publication bias were investigated. We also evaluated the clinical utility of DWI in diagnosis of lung lesions.

Results

Seventeen studies comprising 855 malignant and 322 benign lesions were included in this meta-analysis. There was no significant threshold effect. Summary receiver operating characteristic curve showed that AUC was 0.909 (95% confidence interval [CI], 0.862–0.931). Pooled weighted estimates of sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were 0.828 (95% CI, 0.801–0.853), 0.801 (95% CI, 0.753–0.843), 4.01 (95% CI, 2.78–5.80), and 0.20 (95% CI, 0.15–0.27), respectively. Heterogeneity was found to have stemmed primarily from study design (retrospective or prospective study). Subgroup analysis showed that diagnostic performance (sensitivity, 0.88; 95% CI, 0.82–0.92 and specificity, 0.89; 95% CI, 0.79–0.96) of retrospectively designed studies was significantly higher than that of prospectively designed studies. The Deeks’ funnel plot indicated the absence of publication bias.

Conclusions

With respect to the accuracy and DOR, DWI is useful for differentiation between malignant and benign pulmonary nodules or masses. Diagnostic test accuracy is not the be-all and end-all of diagnostic testing. Concerning PLR and NLR, DWI may not help to alter posttest probability compared to pretest probability to sufficiently alter physician’s decision making. Future analyses should be conducted in large-scale, high-quality trials to evaluate its clinical value and establish standards of DWI measurement, analysis, and cutoff values of diagnosis.

Pulmonary malignant lesion, especially lung cancer, is one of the leading causes of death . It usually presents as a solid nodule or mass on chest radiography or computed tomography (CT). Nowadays, there are mainly two common noninvasive methods for examining pulmonary nodules: positron emission tomography (PET) and CT . However, PET has been known to give false-positive results in inflammatory nodules or masses and false-negative results in well-differentiated pulmonary adenocarcinoma . In addition, high cost of fluorodeoxyglucose-PET restricts its clinical application . Although many well-known characteristics based on shape, size, and internal characteristics have been described and proven to be useful for lesion differentiation on contrast-enhanced CT, it remains a challenge for radiologists and clinicians to differentiate malignant and benign lesions on CT because there are some overlaps especially between hypervascular benign tumors or active granulomas and malignant nodules . Moreover, both PET and CT are radioactive. For these reasons, a nonradioactive and accurate alternative method is still desirable.

Recently, diffusion-weighted magnetic resonance imaging (DWI) has been promoted as an advantageous method, which is applied to various body parts for clinical use. Advantages of DWI lie in neither delivering radiation doses nor requiring exogenous contrast medium. As a new type of magnetic resonance imaging (MRI) functional imaging, it can provide quantitative and qualitative information about the integrity of cell membranes and tissue consistency. In the past, lung/chest imaging was the contraindication of MRI. Nevertheless, recent advances in fast imaging techniques such as echo planar imaging and parallel imaging make MRI more suitable for lung/chest imaging . Many studies have explored the role of DWI in differentiation of malignant and benign lesions, but there are some inconclusive or conflicting results published . Our study objective was to perform a meta-analysis to derive a more comprehensive and precise assessment for the overall diagnostic accuracy of DWI in the diagnosis of malignant pulmonary nodules or masses.

Methods

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Inclusion and Exclusion Criteria

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Data Extraction and Quality Assessment

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Meta-analysis

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Result

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Figure 1, Flow chart of study identification, inclusion, and exclusion.

Table 1

Characteristics of Studies Included in Meta-analysis

Study ID First Author (Ref.) Year Patients ( n ) Nation Lesion Number Study Design Blind b Value Field Strength QUADAS Score DWI Positivity Cutoff (ADC × 10 3 mm/s 2 ) 1 Tondo 2011 34 Italy 34 Retrospective ND 500\1000 1.5 10 ADC 1.25 2 Regier 2011 20 Germany 40 Retrospective ND ND 1.5 10 ADC ND 3 Ohba 2011 58 Japan 76 Prospective Y 1000 1.5 12 ADC 1.1 4 Liu 2010 62 China 66 ND Y 500 1.5 11 ADC 1.4 5 Koyama 2010 32 Japan 33 ND ND 1000 1.5 11 ADC 1.4 6 Uto 2009 28 Japan 28 Prospective ND 1000 1.5 10 LSR/ADC 0.834 7 Tanaka 2009 46 Japan 46 Retrospective Y ND 1.5 12 LSR ND 8 Ohba 2009 110 Japan 124 Retrospective Y 1000 1.5 11 ADC 1.4 9 Satoh 2008 51 Japan 54 ND Y 1000 1.5 13 LSR ND 10 Mori 2008 104 Japan 140 Prospective Y 1000 1.5 11 ADC 1.1 11 Wu 2010 61 China 61 ND Y 500 3 9 ADC 1.38 12 Wang 2008 56 China 56 ND Y 300\500\900 3 9 LMR ND 13 Chen 2011 58 China 58 ND Y 300\600 1.5 10 LSR/LMR ND 14 Cai 2011 97 China 97 Retrospective Y 600 1.5 9 ADC 1.5 15 Li 2011 116 China 120 ND Y 500\1000 3 9 ADC 1.47 16 Deng 2012 77 China 77 ND Y 500 1.5 9 ADC 1.49 17 Gümüştaş 2012 67 Turkey 67 Prospective Y 500\1000 1.5 11 LMR ND

ADC, apparent diffusion coefficient; DWI, diffusion-weighted magnetic resonance imaging; LMR, signal intensity ratio of lesion in muscle; LSR, signal intensity ratio of lesion in spinal cord; ND, no data; QUADAS, quality assessment of diagnostic studies; Y, yes.

Table 2

Raw Data of Diagnostic Performance of Studies Included in This Meta-analysis

Study ID First Author (Ref.) Year TP FP FN TN 1 Tondo 2011 27 0 3 4 2 Regier 2011 24 1 3 12 3 Ohba 2011 53 2 5 16 4 Liu 2010 45 3 9 9 5 Koyama 2010 13 2 10 8 6 Uto 2009 15 1 3 9 7 Tanaka 2009 32 3 1 10 8 Ohba 2009 70 1 26 27 9 Satoh 2008 32 7 4 11 10 Mori 2008 74 1 32 33 11 Wu 2010 33 4 5 19 12 Wang 2008 33 6 5 12 13 Chen 2011 31 11 7 9 14 Cai 2011 52 7 9 29 15 Li 2011 80 3 20 17 16 Deng 2012 48 7 3 19 17 Gümüştaş 2012 46 5 2 14

FN, false negative; FP, false positive; TN, true negative; TP, true positive.

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Threshold Effect Analysis

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Figure 2, Hierarchical summary receiver operating characteristic (HSROC) curves of diffusion-weighted magnetic resonance imaging.

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Diagnostic Accuracy of DWI

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Figure 3, Forest plot of sensitivity of diffusion-weighted magnetic resonance imaging. Circles indicate point estimate of each study. The size of circle is proportional to 1/(within-study variance + between-study variance estimate), indicating relative contribution of study to meta-analysis. Solid horizontal lines represent 95% confidence intervals (CIs). Diamond indicates pooled sensitivity. df, degrees of freedom.

Figure 4, Forest plot of specificity of diffusion-weighted magnetic resonance imaging. Circles indicate point estimate of each study. The size of circle is proportional to 1/(within-study variance + between-study variance estimate), indicating relative contribution of study to meta-analysis. Solid horizontal lines represent 95% confidence intervals (CIs). Diamond indicates pooled sensitivity. df, degrees of freedom.

Figure 5, Forest plot of odds ratio (OR) of magnetic resonance imaging. Circles indicate point estimate of each study. The size of circle is proportional to 1/(within-study variance + between-study variance estimate), indicating relative contribution of study to meta-analysis. Solid horizontal lines represent 95% confidence intervals (CIs). Diamond indicates pooled diagnostic OR.

Figure 6, Scattergram of the positive likelihood ratio (PLR) and negative likelihood ratio (NLR). Pooled estimates for the diffusion-weighted magnetic resonance imaging test were as follows: PLR of 4.01 (95% CI, 2.78–5.80); NLR of 0.20 (95% CI, 0.15–0.27). CI, confidence interval; LLQ, left lower quadrant; LUQ, left upper quadrant; RLQ, right upper quadrant; RUQ, right upper quadrant.

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Meta-regression and Subgroup Analyses

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Table 3

Result of Meta-regression Analysis

Variable Coefficient Standard Error_P_ DOR 95% CI Study design 0.975 0.289 .006 2.65 1.41–4.97 Blind 0.237 0.633 .715 1.27 0.32–5.04 Field strength 0.347 0.345 .334 1.42 0.67–3.01 DWI positivity −0.656 0.416 .141 0.52 0.21–1.28

CI, confidence interval; DWI, diffusion-weighted magnetic resonance imaging; DOR, diagnostic odds ratio.

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Evaluation of Clinical Utility

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Figure 7, Fagan nomograms plotted to calculated posttest probabilities using different pretest probabilities of malignant pulmonary lesion in three clinical scenarios. A, b, and c represented 25%, 50%, and 75% pretest probability, respectively. LR, likelihood ratio; prob, probability; post_prob_pos, posttest probability–positive LR; post_prob_neg, posttest probability–negative LR.

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Publication Bias

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Figure 8, Deeks' funnel plot test for asymmetry, as determined by linear regression of inverse root of effective sample sizes (ESS) on log diagnostic odds ratio. The result indicates the absence of publication bias ( P = .498).

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Discussion

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Conclusion

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Acknowledgments

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