Gadolinium enhancement in MR imaging is a common outcome in clinical trials for MS treatments as well as a marker of disease activity in clinical practice. Successful prediction of enhancement from standard MR imaging sequences performed without gadolinium injection would indicate that those sequences are both sensitive to and specific for tissue changes that occur in the setting of acute inflammatory lesions. Previous studies have demonstrated sensitivity of both T1- and T2-weighted scans to these changes. Our results indicate that the findings on these scans are also somewhat specific and that in the proper context and with careful interpretation, the presence or absence of enhancement may, in many cases, be inferred. For example, a lesion that is both new (ie, not present on a prior scan) and predicted to enhance may very likely truly enhance.
The modeling indicates that voxels that are moderately but not extremely hypointense on T1-weighted images and hyperintense on T2-weighted images are most likely to enhance. This finding is evident from the raw images and probably reflects the presence of increased extracellular water with only limited tissue loss in acute lesions. Water, in the form of edema, may be found in the interstitial space but also between the myelin sheets.1,2
However, the presence of edema does not in itself imply vascular permeability, and there are many neurologic conditions in which edema is present without frank BBB opening as detected by gadolinium enhancement. Nevertheless, in the context of acute MS lesions, it may be the case that this edema directly reflects the presence of an open BBB.
From a technical point of view, our methods are computationally fast and automatic. Although training our classifier is relatively slow (by using modern computing facilities, it takes minutes to hours depending on the number of scans), the training is a 1-time procedure and the fitted model may be expressed as the 4 coefficients in model 1. The estimated coefficients were optimized for our protocol and may be useful for others, but proper training on different protocols is necessary for the performance of the classification tool. On the other hand, given a new scan, a personal computer would take only seconds to conduct the prediction based on the estimates provided in the On-line Appendix
One way to substantially increase the power of our method would be to combine it with subtraction imaging, which can more accurately detect and identify new lesions.14
Limiting the prediction of enhancement to lesions that are new or changed since the previous scan would likely reduce the number of false-positive results at both levels of analysis (scan and voxel). On the other hand, restricting our analysis to new or changed lesions would affect the NPV because it would change the proportion of lesions considered that are truly enhancing. Thus, the magnitude of improvement in the performance is hard to predict. Inclusion of additional unenhanced MR imaging contrasts, such as those provided by proton attenuation–weighted, diffusion-weighted, perfusion-weighted (eg, arterial spin-labeling), and magnetization-transfer imaging, which have differential sensitivity to the types of tissue damage that occur within MS lesions, might also improve the prediction accuracy.
There are some technical limitations to the methods proposed in this article. The first is that to apply our methods optimally, removal of extracerebral tissues is required. In our implementation, we used a skull-stripping algorithm followed by an erosion procedure to remove not only the scalp, skull, and meninges but also some of the cortical mantle, which only rarely enhances in MS. However, some extracerebral tissue, such as the interhemispheric meninges, was not removed by this procedure, and this was the source of many of the voxels falsely identified as enhancing. Thus, it remains necessary to inspect the spatial location of predicted enhancements to verify that they are in the brain.
A limitation of the scan-level predictor presented in this article is that it is defined on the basis of the voxel-level classifier. This may not be optimal because spatial correlation is an important factor. Our methods address this through spatial smoothing, but more sophisticated methods may perform better. As functional and image regression techniques are developed in the statistical literature,15
they may yield further improvement in the prediction of enhancement and measurement of BBB abnormality.
A final limitation is the use of postcontrast T2-weighted FLAIR scans to identify candidate-enhancing voxels, which we define to be those with signal intensities in the highest percentile. On the basis of our experience and on published reports,10,11
we believe that the presence of gadolinium does not substantially alter this identification because lesions only become more hyperintense when they enhance. Unfortunately, precontrast T2-weighted FLAIR scans were not available for analysis so that direct verification of this observation on the current dataset is not possible.