Geography-based Assessment of Cerebral Infarction Improves Correlation With Clinical Outcome
N. M. Menezes1, C. J. Lopez2, T. Benner2, R. Wang2, O. Wu2, M. Zhu2, H. Aronen3, J. Karonen4, Y. Liu4, J. Nuutinen4, A. G. Sorensen2
1
Harvard - Massachusetts Institute of Technology, Cambridge, MA, United States, 2Massachusetts General Hospital, NMR Center, Boston, MA, United States,
3
Helsinki Central University Hospital, Helsinki, Finland, 4Kuopio University Hospital, Kuopio, Finland
Synopsis: Lesion size is moderately correlated with clinical outcome in acute stroke. Here we demonstrate that the correlation between cerebral
infarction on MRI and clinical outcome improves when lesion location is accounted for. We developed a voxel-by-voxel ‘hazard atlas’ from T2
images of 12 acute stroke patients. After coregistration, the volumes occupied by lesions were weighted according to clinical outcome as measured
by the NIH Stroke Scale to create an atlas. This atlas was then applied to a different set of 12 patients to generate a ‘hazard score’ that correlated
better with observed NIH Stroke Scale Scores than lesion volume alone.
Introduction: Lesion size is correlated with clinical outcome in human acute stroke (and other diseases). However, the correlation is only moderate,
with a high variance [1]. We hypothesized that the correlation between lesion size and clinical outcome would be higher if lesion location were taken
into account. Since the brain contains anatomically distinct processing areas, it seems likely that lesions in processing areas that are closely
responsible for the tasks that are heavily weighted in outcome scores would have a larger impact than lesions in regions that were less weighted. We
therefore sought to test this hypothesis by building a brain atlas that would indicate the relative importance to outcome scores of lesions in each part
of the brain. We then tested this atlas on additional stroke patients to generate a ‘hazard score’ from both lesion size and lesion location to determine
whether the correlation between hazard score and clinical outcome was higher than that between lesion size and clinical outcome.
Methods: 12 T2 weighted images, termed the ‘training set’, were retrospectively reviewed in 12 patients an average 8 days after onset of symptoms.
On each image, the lesion was outlined and a binary mask created (1=infarct, 0=no infarct). Data set co-registration was performed using FLIRT
software (Image Analysis Group, FMRIB, Oxford, UK). The result was then used to create a 3D atlas of the brain of approximately 1 mm resolution,
where each voxel represented the likelihood of an infarct in that area contributing to a given outcome score. This was done by multiplying each
abnormal voxel in the input binary data set by a coefficient corresponding to the NIH Stroke Scale Score (NIHSSS) divided by the number of
abnormal voxels. Thus the location of a single voxel that was associated with a high NIHSSS would get proportionately greater weight than a large
lesion that had a low NIHSSS. This process was repeated for each of the 12 MRI scans in the training set, and the atlas was then normalized to make
the highest voxel in the atlas a value of 1.0. An additional 12 MRI images were retrospectively reviewed in 12 patients (different from ‘training set’,
also average 8 days post onset). For these images, co-registration and labeling of the infarct was performed as in the training data set. A ‘hazard
score’ was calculated for each MRI scan by multiplying each voxel in the identified lesion by the appropriate coefficient from the atlas described
above and computing the sum. Correlation analysis was then performed for infarct size vs. NIHSSS and ‘hazard score’ vs. NIHSSS.
Lesion Volume vs. NIHSS Score
Results: The correlation between infarct size and NIHSSS was R2=0.55. The
correlation between the hazard score and NIHSSS was R2=0.69. Fig 1 (top)
shows the two correlation graphs. Fig 2 shows 5 representative slices from the
atlas, indicating that higher coefficients are present near the motor cortex as
might be expected from the NIHSSS that is heavily weighted to motor function.
'Hazard Score' vs. NIHSS Score
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R2 = 0.552
R2 = 0.6893
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NIHSS Score
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Discussion: The relationship between infarct location and clinical impact has
long been acknowledged. Nevertheless, most clinical trials that include imaging
still use infarct size as a biomarker of outcome. An imaging biomarker with
such a variable relationship to clinically meaningful endpoints, unfortunately,
requires larger sample sizes to determine statistical significance. By using this
‘hazard atlas’ imaging may prove to be a superior surrogate endpoint.
Furthermore, risk maps that quantify the imaging correlate to the ‘ischemic
penumbra’ [2] could be linked by means of our hazard atlas to the potential
clinical impact of treating or not treating the mismatch. This could allow the
generation of prospective hazard scores, thus allowing clinicians to assess
quantitatively the impact of treatment decisions. Geography may also play a
role in diffusion-perfusion mismatch outcomes; that is, the location of a
mismatch in the brain might affect its likelihood of proceeding to infarction,
something that could also be assessed using our atlas approach.
While the focal geography of an infarct definitely plays a role in its clinical
impact, many brain functions are not anatomically distinct, thus limiting our
current approach. Activation studies have shown that brain function often
requires an intact network of anatomically distinct areas working in concert; the
simple atlas-building approach outlined here does not identify such networks easily, but more sophisticated atlas-building methods may capture such
information. Moreover, our approach does not yet take into account plasticity or stroke recovery subsequent to the scan time. Further work is
underway to compare atlases built on acute NIHSS scores vs. chronic NIHSS scores as this may better indicate the role of recovery. The NIHSS also
measures brain function relatively crudely. Atlases could easily be built with more sophisticated behavioral scales, (e.g. Fugl-Meyer), and tailored to
different functions (motor, vision, speech, memory, etc) and could help quantify the impact on outcome that infarction in a given brain region might
have. Additional work is also needed in many aspects of the atlas generation, such as the use of diffusion or FLAIR images as input and the
incorporation of other data that may play a role, including stroke pathophysiology (e.g., embolic versus thrombotic), patient demographics (e.g., age,
gender), concomitant medications, etc., as these could also lead to differential outcomes. Finally, we believe that the use of such atlases may allow
the generation and testing of new hypotheses to better understand—and eventually to better treat—human acute cerebral ischemia.
References: 1. Saver et al, Stroke 1999, 30:293. 2. Wu et al, Stroke 2001, 32:933.
Proc. Intl. Soc. Mag. Reson. Med. 11 (2003)
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