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Diagnostic Accuracy Using Diffusion Tensor Imaging in the Diagnosis of ALS

Posted Aug 31, 2012 Updated Apr 10, 2023
By Bradley R. Foerster MD , Ben A. Dwamena MD , Myria Petrou MA MBChB MS , Ruth C. Carlos MD MS , Brian C. Callaghan MD MS , Martin G. Pomper MD PhD
18 min read

Rationale and Objectives

A number of studies have reported decreases in fractional anistropy (FA) in amyotrophic lateral sclerosis using diffusion tensor imaging (DTI). The purpose of this study was to perform a meta-analysis in order to estimate the diagnostic test accuracy measures of DTI for the diagnosis of amyotrophic lateral sclerosis (ALS).

Materials and Methods

We searched MEDLINE (1966–April 2011), EMBASE (1999–April 2011), CINAHL (1999–April 2011), and Cochrane (2005–April 2011) databases to identify studies that measured FA in ALS subjects. Human, single-center studies using a DTI region of interest (ROI) or tractography techniques were used to compare FA values along the brain corticospinal tracts between ALS subjects and healthy controls. There were no language restrictions. Independent extraction of articles by 2 authors using predefined data fields including study quality indicators. We identified 30 case-control studies that used region of interest or tractography DTI techniques. We applied binormal receiver operative characteristic (ROC) curve analysis to assign specificity and sensitivity for each study. We applied the bivariate mixed-effects regression model using the Markov Chain Monte Carlo Simulation to calculate summary estimates for the sensitivity and specificity. We used the metan module in Stata, version 11.0, to calculate the area under the ROC curve, diagnostic odds ratio and the test effectiveness summary estimates.

Results

The pooled sensitivity was 0.65 (95% CI 0.61–0.69); the pooled specificity, 0.67 (95% CI 0.63–0.72); the pooled diagnostic odds ratio, 1.88 (95% CI 1.46–2.30); the pooled test effectiveness, 1.04 (95% CI 0.81–1.27); and the pooled area under the ROC curve, 0.76 (95% CI 0.71–0.81). Subanalyses comparing magnetic resonance imaging (MRI) field strength (1.5T vs. 3.0T) and brain location (corticospinal tract average vs. internal capsule) revealed no significant differences in the test accuracy measures. Reference standard used for the diagnosis of ALS was the El Escorial criteria. There was at least moderate heterogeneity between the studies. True study quality is uncertain.

Conclusion

The discriminatory capability of DTI to make a diagnosis of ALS is only modest. There were no significant differences in the diagnostic test accuracy summary estimates with respect to MRI field strength or brain location.

Amyotrophic lateral sclerosis (ALS), a neurodegenerative condition of the corticospinal tract and spinal anterior horn cells, presents with varying degrees of lower motor neuron (LMN) and upper motor neuron (UMN) signs . The incidence of ALS is estimated to be 1.5–2.7 per 100,000 , with a uniformly fatal outcome. Mean survival of 2–4 years after initial diagnosis has been reported . Delay between onset of symptoms and diagnosis reaches one year , which in part is due to an absence of reliable biomarkers. Thus, objective diagnostic tests to establish and corroborate extent of UMN disease are needed .

Diffusion tensor imaging (DTI), an advanced neuroimaging technique, quantifies the local microenvironmental characteristics of water diffusion and evaluates the integrity of white matter fiber tracts . DTI holds promise as a potential biomarker to detect the pathologic changes in ALS, particularly UMN involvement . One of the key measurements provided by DTI is the fractional anisotropy (FA), which provides a scalar measurement of the degree of water diffusion . When the integrity of the white matter tracts is compromised by disease processes that interfere with water diffusion directionality, the FA is reduced . Over the past decade, a number of published studies have described decreases in the FA of the brain’s corticospinal tract in ALS. However, the vast majority of the publications do not report the test accuracy of DTI to differentiate between diseased and nondiseased populations. In particular, the diagnostic value of DTI in patients presenting with possible or “early” signs of ALS is unknown. If the diagnostic test accuracy measures of this advanced neuroimaging technique are adequate, DTI could be used in the clinical workup of suspected ALS similar to electromyography. The purpose of this study was to aggregate the reported data across the different studies and to estimate summary diagnostic test accuracy measures of DTI in the diagnosis of ALS using standard meta-analysis techniques. Our secondary aims were to investigate study-specific sources of heterogeneity in test accuracy measures.

Methods

Study Search Strategy

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Study Selection

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Data Extraction

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Assessment of Methodologic Quality

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Statistical Analysis

Study level analysis

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Meta-analysis model

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Assessment of Heterogeneity

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Testing for Publication Bias

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Results

Study Selection

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Figure 1, Flowchart illustrates the selection of studies. DTI, diffusion tensor imaging; ROI, region of interest.

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Study Characteristics

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Table 1

Individual Study Characteristics

Author Year Origin MRI b-Value Number of Directions DTI Analysis Method Agosta et al 2007 Italy 1.5T 900 12 ROI–visual Agosta et al 2009 Italy 1.5T Unclear Unclear Tractography Aoki et al 2005 Japan 1.5T 1000 13 ROI–tractography Bartels et al 2008 Germany 2.9T 1000 24 ROI–tractography Blain et al 2007 UK 1.5T 1300 64 ROI–visual Blain et al 2011 UK 1.5T 1300 64 Tractography Ciccarelli et al 2006 UK 1.5T 1150 54 Tractography Cosotinni et al 2005 Italy 1.5T 1000 25 ROI–visual Cosottini et al 2010 Italy 1.5T 1000 31 ROI–visual Ellis et al 1999 UK 1.5T 620 7 ROI–visual Filippini et al 2010 UK 3.0T 1000 60 Tractography Garcia et al 2007 Brazil 1.5T 1000 12 ROI–visual Graham et al 2004 UK 1.5T 1200 54 ROI–visual Hong et al 2008 S. Korea 3.0T 1000 25 ROI–tractography Ito et al 2008 Japan 3.0T 700 6 ROI–tractography Karlsborg et al 2004 Denmark 1.5T 550 6 ROI–visual Lombardo et al 2009 Italy 1.5T 1000 25 ROI–visual Metwalli et al 2010 USA 3.0T 1000 64 ROI–visual Nair et al 2010 USA 3.0T 1000 30 ROI–visual Nelles et al 2008 Germany 3.0T 600 16 Tractography Pyra et al 2010 Canada 1.5T 1000 6 ROI–visual Roccatagliata et al 2009 Italy 1.5T 1000 51 ROI–visual Sage et al 2007 Belgium 3.0T 800 16 Tractography Schimrigk et al 2007 Germany 1.5T 1000 6 ROI–tractography Senda et al 2009 Japan 3.0T 700 6 ROI–tractography Valsasina et al 2007 Italy 1.5T 900 12 ROI–visual Wang et al 2006 USA 3.0T 1000 12 ROI–tractography Wong et al 2007 Canada 1.5T 1000 6 ROI–visual Woolley et al 2011 USA 4.0T 800 6 ROI–visual Yin et al 2004 China 1.5T 1000 25 ROI–visual

MRI, magnetic resonance imaging; ROI, region of interest; ROI–tractography, used tractography generated fiber tracts to aid placement of ROIs; ROI–visual, placed ROIs manually using visual inspection of structural imaging; Tractography, used tractography techniques to generate fiber tracts to directly calculate fractional anisotropy values; UK, United Kingdom; USA, United States of America.

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Diagnostic Test Performance Indexes and Summary Estimates

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Figure 2, Area under the summary ROC curve for DTI. Each circle represents an individual study result. The diamond in the center represents intersection of the summary sensitivity and specificity, the inner dark gray circle represents the 95% confidence interval of the summary sensitivity and specificity, and the outer light gray circle represents the 95% predicted interval. SENS, sensitivity; SPEC, specificity; SROC, summary receiver operating characteristic; AUC, area under the curve.

Figure 3, Forest plot of AUC for diagnosis of ALS using DTI-generated fractional anisotropy values. The center point represents the point estimated AUC for the respective study and the horizontal line, the 95% confidence interval (CI) for the respective study. The vertical broken line represents the pooled AUC and the boundaries of the hollow diamond represent the 95% CI of the pooled results. nd, number of diseased; meand, mean fractional anistropy (FA) of diseased; sdd, standard deviation FA of diseased controls; nh, number of healthy controls; meanh, mean FA of healthy controls; sdh, standard deviation FA of healthy controls; AUC, area under the curve.

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Effect of Field Strength on Diagnostic Performance (1.5T versus 3.0T)

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Figure 4, AUC Forest plots for (a) 1.5T and 3.0T MRI field strength and (b) corticospinal tract and internal capsule. The center point represents the point estimated AUC for the respective study and; the horizontal line , the 95% confidence interval (CI) for the respective study. The vertical broken line represents the pooled AUC and the boundaries of the hollow diamond represent the 95% CI of the pooled results. nd, number of diseased; meand, mean fractional anistropy (FA) of diseased; sdd, standard deviation FA of diseased; nh, number of healthy controls; meanh, mean FA of healthy controls; sdh, standard deviation FA of healthy controls; AUC, area under the curve.

Table 2

Sensitivity and Specificity Measures

Sensitivity Specificity Overall 0.65 (0.61–0.69) 0.67 (0.63–0.72) 1.5T 0.65 (0.59–0.71) 0.70 (0.61–0.74) 3.0T 0.70 (0.61–0.73) 0.71 (0.57–0.80) CST 0.68 (0.63–0.74) 0.72 (0.68–0.76) IC 0.65 (0.54–0.73) 0.66 (0.60–0.73)

CST, corticospinal tract; IC, internal capsule.

Table 3

Diagnostic Odds Ratio and Test Effectiveness Measures

Diagnostic Odds Ratio Test Effectiveness Overall 1.88 (1.46–2.30) 1.04 (0.81–1.27) 1.5T 2.03 (1.45–2.61) 1.12 (0.80–1.44) 3.0T 1.92 (1.36–2.48) 1.06 (0.75–1.37) CST 2.18 (1.56–2.81) 1.20 (0.86–1.55) IC 1.93 (1.29–2.56) 1.06 (0.71–1.41)

CST, corticospinal tract; IC, internal capsule.

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Effect of Brain Location on Diagnostic Performance (Corticospinal Tract Average versus Internal Capsule)

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Assessment of Methodological Quality

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Figure 5, Study quality scores. Graph illustrates study quality based on QUADAS criteria, expressed as a percent of studies meeting each criterion.

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Assessment of Heterogeneity

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Publication Bias

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Figure 6, Assessing publication bias. The funnel plot horizontal axis expresses treatment effect, in this instance, measured by area under the receiver operating characteristic curve (AUC). The vertical axis expresses study size, as measured by standard error (SE). Studies with larger standard errors have a wider confidence interval from smaller sample size. The graphed vertical line represents the observed mean AUC and the dashed lines represent the 95% confidence interval limits for the expected distribution for published studies. The points represent the observed distribution of the published studies. Visual inspection of the plot demonstrates the presence of publication bias, with many studies outside the 95% confidence interval limits. Further, the plot demonstrates that studies with smaller study size (ie, larger standard errors) have lower test performance (ie, AUC).

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Discussion

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Figure 7, Posttest probabilities after diffusion tensor imaging (DTI) for hypothetical populations with different prevalence of disease according to Bayes theorem.

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Acknowledgments

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Appendix 1

Statistical Approach for Binormal Curve Analysis

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Supplementary data

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Appendix Figure 1, Forest plot of sensitivity and specificity. Figure depicts sensitivity, specificity, true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) using generated thresholds expressed at the individual study level.

Appendix Figure 2, Forest plot of diagnostic odds ratio. Figure depicts diagnostic odds ratios, expressed at the individual study level. ND, number of diseased; meand, mean fractional anistropy (FA) of diseased; SDD, standard deviation FA of diseased; NH, number of healthy controls; meanh = mean FA of healthy controls; SDD, standard deviation FA of healthy controls; DOR, diagnostic odds ratio.

Appendix Figure 3, Forest plot of test effectiveness. Figure depicts study quality based on QUADAS criteria, expressed at the individual study level. ND, number of diseased; meand, mean fractional anistropy (FA) of diseased; SDD, standard deviation FA of diseased; NH, number of healthy controls; meanh = mean FA of healthy controls; SDD, standard deviation FA of healthy controls; TE, test effectiveness.

Appendix Figure 4, Individual study quality score. Figure depicts study quality based on QUADAS criteria, expressed at the individual study level.

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