1. health and fitness
2. disease

doi:10.1093/brain/awl183
Brain (2006), 129, 2384–2393
Characterizing physiological heterogeneity of
infarction risk in acute human ischaemic
stroke using MRI
Ona Wu,1,2 Søren Christensen,1 Niels Hjort,1 Rick M. Dijkhuizen,2 Thomas Kucinski,3 Jens Fiehler,3
Go¨tz Thomalla,4 Joachim Ro¨ther4 and Leif Østergaard1
1
˚ rhus University Hospital,
Center for Functionally Integrative Neuroscience, Department of Neuroradiology, A
˚ rhus C, Denmark, 2Image Sciences Institute, University Medical Center Utrecht, Utrecht,
A
The Netherlands, 3Departments of Neuroradiology and 4Departments of Neurology,
University Medical Center Hamburg-Eppendorf, Hamburg, Germany
˚ rhus University Hospital,
Correspondence to: Ona Wu, PhD, Center for Functionally Integrative Neuroscience, A
Building 30, Nørrebrogade 44, 8000 srhus C, Denmark
E-mail: ona@pet.auh.dk
Keywords: mathematical modelling; cerebral ischaemia; magnetic resonance imaging; thrombolytic therapy;
outcome measures
Abbreviations: ADC = apparent diffusion coefficient; AUC = area under the ROC curve; CBF = cerebral blood flow;
CBV = cerebral blood volume; DELAY = tracer arrival delay; DWI = diffusion-weighted MRI; GLM = generalized linear model;
iDWI = isotropic DWI; IQR = interquartile range; MLV = measured lesion volume; MTT = mean transit time;
NIHSSS = National Institutes of Health Stroke Scale Score; PI = prediction interval; PLV = predicted lesion volume;
PWI = perfusion-weighted MRI; ROC = receiver operating characteristic; rt-PA = recombinant tissue plasminogen activator;
T2 EPI = T2-weighted image; TIMI = thrombolysis in myocardial infarction; WM = white matter
Received January 14, 2006. Revised June 2, 2006. Accepted June 9, 2006. Advance Access publication August 3, 2006.
# The Author (2006). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
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Viable tissues at risk of infarction in acute stroke patients have been hypothesized to be detectable as
volumetric mismatches between lesions on perfusion-weighted (PWI) and diffusion-weighted magnetic resonance imaging (DWI). Because tissue response to ischaemic injury and to therapeutic intervention is tissue- and
patient-dependent, changes in infarct progression due to treatment may be better detected with voxel-based
methods than with volumetric mismatches. Acute DWI and PWI were combined using a generalized
linear model (GLM) to predict infarction risk on a voxel-wise basis for patients treated either with nonthrombolytic (Group 1; n = 11) or with thrombolytic therapy (Group 2; n = 27). Predicted infarction risk
for both groups was evaluated in four ipsilateral regions of interest: tissue acutely abnormal on DWI
(Core), tissue acutely abnormal on PWI but normal on DWI that either infarcts (Recruited) or does not
(Salvaged), and tissue normal on both DWI and PWI that does not infarct (Normal) by follow-up imaging
> 5 days. The performance of the models was significantly reduced for the thrombolysed group compared with
the group receiving standard treatment, suggesting an alteration in natural progression of the ischaemic
cascade. Average GLM-predicted infarction risk values in the four regions were different from one another
for both groups. GLM-predicted infarction risk in Salvaged tissue was significantly higher (P = 0.02) for thrombolysed patients than for non-thrombolysed patients, suggesting that thrombolysis rescued tissue with higher
infarction risk than typically measured in tissue that spontaneously recovered. The observed spatial
heterogeneity of GLM-predicted infarction risk values probably reflects the varying degrees of tissue injury
and salvageability that exist after stroke. MRI-based algorithms may therefore provide a more sensitive means
for monitoring therapeutic effects on a voxel-wise basis.
Spatial heterogeneity of cerebral infarction risk
Brain (2006), 129, 2384–2393
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Introduction
Material and methods
Patients
We retrospectively studied consecutive acute stroke patients from
January 2000 to January 2001 who received CT, DWI and PWI
within 6 h of symptom onset, as well as follow-up studies at
24 h (Day 1) and at least 5 days (F/U). Findings on individual
imaging modalities have been previously reported for these patients
(Fiehler et al., 2002a, b; 2004a, b). Patients who developed type 2
parenchymal haematomas, associated with altering the clinical
course of ischaemic stroke (Fiorelli et al., 1999), were excluded.
Patients were treated either with non-interventional standard
medical treatment (i.e. no thrombolysis) (Group 1, n = 11) or
with intravenous recombinant tissue plasminogen activator
(rt-PA) therapy (Group 2, n = 27). Thrombolysis was performed
<3 h according to ECASS II criteria (Hacke et al., 1998). For
patients where MRI was the primary and only imaging modality,
modified criteria were used, excluding patients from thrombolysis
with signs of intracerebral haemorrhage on MRI and those with
DWI lesions >50% of the MCA territory. Thrombolysis beyond
3 h was performed if the PWI lesion volume exceeded the DWI
lesion by >20%, the DWI lesion was smaller than one-third of
the middle cerebral artery territory, and informed consent was
obtained. The National Institutes of Health Stroke Scale Score
(NIHSSS) was assessed by a stroke neurologist at each imaging
time point. Reperfusion was determined on the basis of PWI
and MR angiography studies acquired on Day 1 and F/U using
modified thrombolysis in myocardial infarction (TIMI) criteria
(Fiehler et al., 2004a). Patients were classified as exhibiting no,
minimal, incomplete or complete reperfusion (TIMI = 0, 1, 2
and 3, respectively). Early reperfusers were defined as patients
with complete reperfusion (TIMI = 3) on Day 1. Information on
reperfusion by Day 1 was not available for one patient in Group 2.
MRI studies
Details of the DWI and PWI imaging protocol have been reported
previously (Fiehler et al., 2002a, b; 2004a, b). Apparent diffusion
coefficient (ADC) maps were calculated from a 3-point fit (bvalues = 0, 500 and 1000 s/mm2) (Fiehler et al., 2002a). The
b-value = 0 and b-value = 1000 DWI images were used as the
T2-weighted image (T2 EPI) and isotropic DWI (iDWI) maps,
respectively. Cerebral blood flow (CBF), cerebral blood volume
(CBV) and mean transit time (MTT) maps were calculated from
signal curves converted to concentration changes over time curves
and deconvolved with an arterial input function selected from the
ipsilateral hemisphere (Østergaard et al., 1996). Tracer arrival delay
(DELAY) (Wu et al., 2003) was determined as the time of the peak of
the residue function.
Generalized linear model analysis
Calculation of the parameters for a generalized linear model (GLM)
has been described previously (Wu et al., 2001). In brief, the outcome status of tissue can be modelled as a binary variable, P, where
the value 1 represents infarcted tissue and the value 0, non-infarcted
tissue. The probability of tissue infarction was represented by the
logistic function
P¼
1
1 þ ehðxÞ
ð1Þ
where h(x), the predictor, is a linear function of its input
parameters, x,
hðxÞ ¼ bT x þ a:
ð2Þ
and b is the vector of calculated coefficients and a is the bias
or intercept term for the GLM. In this study, the input vector, x,
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Early identification of tissue at risk of infarction after acute
stroke has been postulated to be a potential aid to therapeutic
decision-making, thereby improving patient outcome
(Kidwell et al., 2003; Lees et al., 2003; Schellinger et al.,
2003; Levine, 2004). As such, there has been increasing
interest for assisting clinical decision-making with neuroimaging techniques such as diffusion-weighted MRI (DWI),
which is highly sensitive to acute tissue damage, and
perfusion-weighted MRI (PWI), which is sensitive to
haemodynamic disturbance (Fisher, 2003; Hermier et al.,
2003; Nighoghossian et al., 2003; Schellinger et al., 2003;
Levine, 2004; Warach and Baron, 2004; Hjort et al., 2005).
Simple mismatches between larger lesion volumes manifested
in acute PWI than in acute DWI have been speculated to
be useful for identifying, on an individual patient basis, viable
tissue that is at risk of infarction without therapeutic
intervention (Baron and Warach, 2005; Davis et al., 2005;
Hjort et al., 2005). However, studies have shown that these
mismatches, based on dichotomized PWI/DWI parameters,
can underestimate the amount of salvageable tissue (Fiehler
et al., 2002a; Guadagno et al., 2004) or overestimate the
amount of tissue at risk of infarction (Sorensen et al.,
1999; Coutts et al., 2003; Sobesky et al., 2004). This may
be due to heterogeneity of tissue response to ischaemic
injury and to therapeutic intervention (Lo et al., 2005).
Multiparametric algorithms that combine MRI modalities
on a voxel-wise basis have been shown to more accurately
predict risk of infarction in acute human cerebral ischaemia
than when these MRI methods are used separately (Jacobs
et al., 2001; Rose et al., 2001; Wu et al., 2001). Because these
models have high spatial resolution and can therefore capture
intralesional differences, these voxel-wise models may be
able to provide insight into ischaemic lesion evolution
under ‘natural conditions’ as well as to sensitively detect
changes to this process as a result of therapeutic intervention.
The goals of this study were (i) to investigate whether
MR-based algorithms can be used to sensitively monitor
changes in disease progression due to therapeutic intervention on a voxel-wise basis; and (ii) to determine whether
these acutely predicted infarction risk values provide insight
into tissue salvageability. We examined the algorithm’s
predicted risk of infarction using imaging acquired before
treatment in four regions of interest (ROI): (i) the acute
DWI lesion (hypothesized to be tissue that would evolve
to infarction unless rapidly reperfused); (ii) tissue acutely
abnormal on PWI but normal on DWI that is infarcted on
follow-up imaging; (iii) tissue acutely abnormal on PWI
and normal on DWI that is not infarcted on follow-up;
and (iv) normal appearing ipsilateral tissue.
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Brain (2006), 129, 2384–2393
Statistical analysis
Two-tailed Wilcoxon rank-sum tests were used for unpaired
data, and two-tailed Wilcoxon signed rank tests were performed
for pairwise comparisons. Correlation analyses were performed
using Pearson product-moment correlation coefficient. For evaluating the accuracy of the GLM predictions, sensitivity and specificity
of the model for identifying abnormal tissue on F/U were calculated
along with receiver operating characteristic (ROC) curves by varying
the probability threshold between 0 and 100% for classifying
tissue as infarcted. The area under the ROC curves (AUC) that
represents the probability that an image will be correctly classified
normal or abnormal was calculated and compared (Hanley
and McNeil, 1982). To assess accuracy ofPthe predictions, the
root mean square error, RMSE = 1/N H[ (yipi)2], where yi
is the true outcome for the tissue (0 or 1, i.e. not-infarcted
or infarcted), pi is the predicted GLM risk value and N is
the total number of voxels, was calculated and compared across
groups.
The predicted lesion volume (PLV) was defined as tissue
where GLM-predicted infarction risk was >50%. Fifty per cent
was chosen as the threshold since the models were trained to produce the optimal operating point at this cut-off by using an equal
number of infarcted and non-infarcted voxels. Measured lesion
volumes (MLV) were defined as described above for training the
GLM. Initial ischaemic lesions (Core) were defined as hyperintense
regions on acute DWI. Acute PWI lesions were defined as regions of
hyperintensities on acute MTT maps. Acute lesions were initially
demarcated as tissue with values >2 SDs from mean contralateral
values, and then manually adjusted by a neurologist blinded to
the GLM-predicted results to correct for errors in the automatic
outlines due to imaging artefacts. Lesion growth (Recruited) was
defined as infarcted tissue outlined on follow-up not initially present
on the acute DWI. Tissue at risk of infarction that was presumably
salvaged (Salvaged) was defined as areas initially abnormal on the
PWI but normal on follow-up MRI. Normal tissue was defined as
tissue with no apparent abnormalities on acute and follow-up imaging. To minimize errors due to poor co-registration, only patients
with at least 5 cm3 in the Recruited or Salvaged regions were used for
ROI analysis. Differences in MLV, PLV and GLM-predicted values
between early complete reperfusers (TIMI = 3 by Day 1) and the
other patients were also compared.
Results
Median time to MRI for both groups was 3 h [interquartile
range (IQR) = 2.5–3.3 for Group 1 and 2.6–3.6 for Group 2].
Median age in years for Groups 1 and 2 was 60 (Group 1:
IQR = 57–68; Group 2: IQR = 51–68). There was a significant
difference between both groups in the acute NIHSSS, with
more patients presenting with greater severity (P = 0.002) in
the rt-PA treatment group (median = 15, IQR = 12–19) than
in Group 1 (median = 9, IQR = 4–10). In Group 1, five
patients had TIMI = 3 by Day 1 with 8 by F/U. In
Group 2, five patients had TIMI = 3 by Day 1 with 16 by
F/U. No significant difference in initial Core lesion volumes
(Group 1: 34 6 42 cm3; Group 2: 21 6 28 cm3) was found;
however, MTT lesion volumes were significantly smaller
(P = 0.008) for Group 1 (85 6 88 cm3) than Group 2
(165 6 81 cm3) owing to patient selection criteria. No significant difference was found between lesion volumes measured on follow-up (Group 1: 60 6 80 cm3; Group 2: 63 6 71
cm3). Significant differences were found between the seven
acute DWI- and PWI-derived parameters (Fig. 1) for Core,
Recruited and Salvaged ROIs for patients with Recruited and
Salvaged volumes >5 cm3 for Group 1 (n = 7) and Group 2
(n = 22).
The GLM coefficients of the aggregate model derived
from all Group 1 training data were 0.7 6 0.2, 1.2 6
0.2, 6.4 6 0.2, 0.2 6 0.03, 0.1 6 0.03, 1.1 6 0.06 and
0.2 6 0.01 for relative T2 EPI, ADC, iDWI, CBF, CBV,
MTT and DELAY, respectively, with a bias term of 9.5
6 0.3. Three of the jack-knifed models had significantly different coefficients from the aggregate model. Figure 2 shows
examples of GLM-predicted risk of infarction for patients
from both groups involving DWI < PWI who experienced
partial reperfusion (TIMI = 2) by Day 1. The GLM-calculated
risk map encompasses territory that is abnormal on MTT as
well as DWI. For Fig. 2A, the amount of tissue predicted to
infarct corresponds well with the follow-up infarct, while
for the rt-PA-treated patient (Fig. 2B) the amount of tissue
is clearly overestimated. Parts of the initial DWI lesion also
recover (arrow), even though probability of infarction is very
high. Here, lower and upper 95% PIs correspond well with
regions of abnormality on the DWI and MTT maps.
ROC curves of the pooled results across all patients for
Group 1 and Group 2 are shown in Fig. 3. Using a threshold
of 50%, sensitivity and specificity were 77 and 91%, respectively, for Group 1 and 77 and 80% for Group 2. The AUC for
the rt-PA-treatment patients (0.85 6 0.06) was less (P = 0.07)
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consisted of acute T2 EPI, ADC, iDWI, CBF, CBV, MTT and DELAY
maps. All images were co-registered using semi-automated image
registration software [MNI Autoreg (Collins et al., 1994)] to one
another as well as to a probabilistic brain atlas (Mazziotta et al.,
2001). All images were then normalized with respect to mean values
measured in normal contralateral white matter (WM). All input
maps, except DELAY, were normalized by dividing by the mean.
Relative DELAY maps were calculated by subtracting the mean of
contralateral WM values. Training regions were defined as the union
of hyperintensities on the follow-up T2 EPI and iDWI and noninfarcted tissue as remaining ipsilateral hemisphere tissue. Coefficients of the GLM were calculated and then used to estimate risk of
infarction on an individual voxel-wise basis for data from a new
subject. Prediction intervals (PIs) were also produced on a voxelwise basis (MathSoft, 2000). All risk maps were median-filtered to
reduce false-positives due to noise.
To evaluate the performance of the GLMs, a jack-knifing or
leave-one-out approach was followed for Group 1 (Efron, 1982)
to avoid bias that would otherwise occur if a model’s performance
was evaluated on the same data that trained the model. For Group 2,
an aggregate model trained with data from all Group 1 patients was
used. The coefficients for the jack-knifed models were compared
with the coefficients of the aggregate model (one-tailed Z-tests).
For all models, the bootstrapped estimate (Efron, 1982) of the
mean of the GLM coefficients was used, with care taken that an
equal number of infarcted and non-infarcted samples were used
to compensate for imbalanced training data (Japkowicz and
Stephen, 2002).
O. Wu et al.
Spatial heterogeneity of cerebral infarction risk
than that for the non-rt-PA-treated patients (0.90 6 0.05).
The overall RMSE was significantly greater (P = 0.0009) for
Group 2 (0.39 6 0.06) than for Group 1 (0.30 6 0.07).
A significant positive correlation with initial NIHSS score
was found for the average GLM-predicted value in tissue
Brain (2006), 129, 2384–2393
2387
predicted to infarct (r = 0.37; P = 0.03). The volume of
the PLV was also significantly correlated with initial NIHSSS
(r = 0.43, P = 0.01). The PLV for both groups was significantly
correlated with respect to the follow-up lesions (Group 1:
r = 0.90, P = 0.0002; Group 2: r = 0.68, P < 0.0001) though the
Fig. 2 Examples of GLM-predicted risk of infarction along with 95% PI in cases of DWI and PWI mismatch. For clarity, only GLM-predicted
values > 50% are shown overlaid on F/U. (A) Output for non-rt-PA-treated 58-year-old male imaged at 2.5 h, initial NIHSSS = 13,
TIMI = 2 at Day 1 and TIMI = 2 by the 7-day F/U. (B) Output for rt-PA-treated 52-year-old male imaged at 2.8 h, initial NIHSSS = 20,
TIMI = 2 on Day 1 and TIMI = 3 by 7-day F/U. For A, the predicted lesion corresponds well with tissue that is infarcted (regions of
hyperintensity) by F/U, while for B the predicted lesion is much larger than the amount of tissue that actually infarcts. Areas initially
abnormal on DWI may also recover (arrowheads). Regions of the lower 95% PI correspond with acute DWI abnormality, while
regions of the upper 95% PI correspond with acute MTT lesions.
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Fig. 1 Acute DWI and PWI parameter values (mean 6 SD) in Core, Recruited and Salvaged ROIs in patients with Recruited and
Salvaged volumes > 5 cm3 for Group 1 (n = 7) and Group 2 (n = 22) patients. All values were normalized by dividing by mean values
in normal ipsilateral hemisphere except for rDELAY, which was calculated by subtracting mean normal values. *P < 0.05 Core versus
Recruited; †P < 0.05 Core versus Salvaged; §P < 0.05 Recruited versus Salvaged; ‡P < 0.05 versus Normal kP < 0.1 versus Normal;
¶
P < 0.1 Group 1 versus Group 2.
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Brain (2006), 129, 2384–2393
correlation was reduced for the rt-PA-treated group (P =
0.06). The difference between the PLVs and MLVs for
Group 1 (37 6 35 cm3) was significantly (P = 0.01) smaller
than that measured for Group 2 (83 6 55 cm3).
Average GLM-predicted values (Group 1: 0.66 6 0.20,
Group 2: 0.72 6 0.11) were significantly larger (Group 1:
P = 0.001; Group 2: P < 0.0001) for tissue that infarcted
than for ipsilateral tissue that did not infarct (Group 1:
0.22 6 0.07; Group 2: 0.30 6 0.07). For tissue that did
not infarct, GLM-predicted values were significantly higher
(P = 0.009) in rt-PA-treated patients than those for Group 1.
Results from subset analysis in the Core, Recruited, Salvaged
and Normal ipsilateral tissue for both Groups 1 and 2 are
shown in Figure 4 demonstrating spatially heterogeneous
predicted infarction risk with greatest risk in the core of
the infarct and lowest in tissue that did not infarct. Group
2 patients exhibited higher risk in Salvaged ROI than Group 1
(P = 0.02).
Table 1 summarizes differences between early complete
reperfusers (TIMI = 3 by Day 1) and patients who did not
completely reperfuse within 24 h (TIMI 6¼ 3 by Day 1). Owing
to limited number of patients, data from Group 1 and 2 were
combined since no significant differences were found
between Groups 1 and 2 for these three parameters for
each subgroup (early versus non-early). Figure 3 shows the
ROC curves for the pooled results from patients demonstrating early complete reperfusion and those who did not. There
was a significant difference in AUC between Groups 1 and
2 for the non-early reperfusers (P = 0.01) and therefore
Group 1 and 2 ROC results were not combined. For the
early reperfusers, sensitivity and specificity for Group 1
were 74 and 91%, respectively, whereas for the others they
were 79 and 91%, respectively. For Group 2, sensitivity and
specificity were 67 and 81% for the early reperfusers and
77 and 80% for the rest. Figure 5A and B show examples
of predicted infarction risk for patients in both groups
demonstrating early reperfusion. In the case of Fig. 5A, the
patient did not receive rt-PA since he had exhibited
reperfusion signs on the acute MRI and is an example
where DWI > PWI. For Fig. 5B, the patient demonstrated
a large PWI > DWI and was treated with rt-PA. The predicted
risk in tissue that infarcted (yellow arrowheads) is much
higher than that in tissue that was salvaged. Furthermore,
the infarct area corresponds well with tissue abnormal on
the lower 95% PI. For a patient who failed to reperfuse
despite rt-PA treatment (Fig. 6), large GLM-predicted risk
values were measured in the PLV.
Table 1 Differences between early reperfusers (TIMI = 3
Fig. 4 GLM-predicted risk of infarction (mean 6 standard
deviation) in different regions in ipsilateral hemisphere in patients
with Recruited and Salvaged volumes > 5 cm3. GLM-predicted
infarction risk values were greatest in the Core and lowest in
Normal tissue for both groups. Recruited tissue had significantly
greater predicted infarction risk only for Group 2. *P = 0.02,
†
P < 0.0001 Core versus Recruited, Salvaged, Normal. ‡P =
0.02, §P < 0.0001 Normal versus Recruited, Salvaged. **P = 0.08,
k
P < 0.0001 Recruited versus Salvaged. ¶P = 0.02 Group 2 versus
Group 1.
on Day 1) and non-early reperfusers (TIMI = 0, 1, 2 on
Day 1)
Factor
P-value
Early reperfusers Non-early
(early versus
reperfusers
(n = 10)
non-early)
(n = 27)
(mean 6 SD)
(mean 6 SD)
MLV (cm3)
27 6 53
PLV (cm3)
95 6 59
GLM in PLV (a.u.) 0.73 6 0.03
75 6 77
145 6 75
0.78 6 0.06
0.01
0.11
0.04
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Fig. 3 ROC curves reflecting performance of algorithms in
predicting tissue outcome in various patient cohorts. Curves
with smaller areas indicate poorer predictive performance. Marker
represents performance using a 50% threshold.
O. Wu et al.
Spatial heterogeneity of cerebral infarction risk
Brain (2006), 129, 2384–2393
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Fig. 6 Example of GLM-predicted infarction risk for an rt-PA-treated 66-year-old male imaged at 3 h, initial NIHSSS = 22, TIMI = 0 on Day 1,
TIMI = 0 by 8-day F/U, who failed to reperfuse. For clarity, only GLM-predicted values > 50% are shown overlaid on F/U. In this case,
the lower 95% PI is much larger than the initial DWI abnormality.
Discussion
Our results show that infarction risk after stroke is spatially
heterogeneous at the hyperacute stage, consistent with the
notion of an ischaemic core surrounded by a penumbra
of salvageable tissue (Astrup et al., 1981; Hossmann, 1994;
Lo et al., 2005), probably reflecting varying degrees of
tissue injury and salvageability. We observed that tissue
presenting abnormal acute perfusion values that recovered
compared with that which infarcted exhibited significantly
lower GLM-predicted infarction risk values in agreement
with findings from earlier reports (Furlan et al., 1996;
Schaefer et al., 2003; Shimosegawa et al., 2005). We found
that with rt-PA treatment tissue that was more severely
injured and hence at greater risk of infarction, as reflected
by significantly larger GLM-predicted infarction risk values,
was salvaged compared with tissue that spontaneously recovered in the non-rt-PA-treated group, consistent with findings
reported by others (Fiehler et al., 2002a; Butcher et al., 2005;
Loh et al., 2005).
Because of intralesional heterogeneity, effects of therapeutic intervention may not be readily detected with simple
volumetric approaches. Previous attempts to use imaging
as a surrogate end-point for evaluating therapeutic efficacy,
on either a volumetric [Clark et al., 1999; The National
Institute of Neurological Disorders and Stroke (NINDS)
rt-PA Stroke Study Group, 2000; Rother et al., 2002; Lees
et al., 2003] or change from initial baseline infarct volume
basis (Warach et al., 2000), have met with limited success.
Voxel-based approaches, in contrast, may be able to more
sensitively monitor treatment effects by comparing each
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Fig. 5 Examples of GLM-predicted infarction risk and 95% PI for patients demonstrating early complete reperfusion (TIMI = 3).
(A) GLM-predicted risk for 64-year-old male not treated with rt-PA and imaged 4.8 h from stroke onset, and his 6-day follow-up images.
Here, the lower 95% PI is smaller than the initial DWI. (B) GLM-predicted risk map for 61-year-old male treated with rt-PA and
imaged 2 h from onset, and his 10-day follow-up images. It may be noted that tissue that infarcted by F/U (yellow arrowheads) is much higher
in the GLM prediction than in tissue that was salvaged. Regionally, the area corresponds well with tissue abnormal on the lower
95% PI.
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(i) Generation of parametric ADC, CBF, CBV, MTT and
DELAY maps.
(ii) Co-registration of DWI and PWI parameters to one
another and to the probabilistic brain atlas.
(iii) Normalization of input parameters with mean measured values in contralateral WM.
(iv) Application of Equations (1) and (2) to the normalized
and co-registered images.
(v) Median filtering of the predicted risk maps.
The first step would also be necessary in simpler volumetric
analysis, and tools to generate these parametric maps are
available directly from the console for several MR systems.
Co-registration software is also available and automatic
(Collins et al., 1994) and would require only a few minutes
since the acute images are already closely aligned. Because
co-registration to the probabilistic brain atlas does not
require high accuracy since the results are only used to generate WM normalization masks, this step can be performed
rapidly. The normalization step is semi-automated, with the
only user interaction consisting of the specification of contralateral hemisphere. Steps (iv) and (v) do not require user
interaction and can be performed very quickly, on the
order of only a few minutes.
Whether the coefficients derived in this study, or any
other study, can be applied to other centres needs to be
further investigated. The coefficients that we report here
differ from those reported in another study (Wu et al.,
2001) owing to differences in training data, which, in that
earlier study, were normalized to user-defined normal grey
matter; involved spin-echo and gradient-echo PWI maps;
were imbalanced, that is, different amounts of infarcted
and non-infarcted tissue; and involved patients imaged at
later time points (<12 h). One drawback of these models
is that they depend on the training data used to develop
them. We eliminated some of the variability of the earlier
model by switching to objectively determined and predefined normalization regions, only gradient-echo PWI
maps and balanced training sets. However, a few persisting
confounds remain, such as the extent of grey matter and
WM in the lesions used in training the models. Recent
studies have shown that tissue recovery and response to
thrombolytic therapy may be dependent on tissue type
(Bristow et al., 2005; Koga et al., 2005; Arakawa et al.,
2006). We are currently investigating models that explicitly
take into account tissue type (Wu et al., 2004), which
would probably improve predictive performance and
reproducibility.
Another factor influencing the reproducibility of the
training coefficients is the homogeneity of disease aetiology
and pathophysiology of the training data. We found significant differences between the jack-knifed coefficients and
the aggregate model in three patients who demonstrated
early partial or complete reperfusion. With increased
number of patients, the influence of one patient will have
less effect on the generated coefficients. The number of
patients needed to stabilize risk prediction will depend
on the homogeneity of the training data. The greater the
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tissue voxel’s fate with its predicted outcome had it not been
treated, thereby reducing the number of patients needed to
demonstrate therapeutic efficacy by decreasing variability and
hence increasing statistical power. Our findings of reduced
accuracy in the performance of our models for predicting
tissue outcome for Group 2 patients compared with
Group 1 patients, as reflected in reduced RMSE, AUC and
correlation with follow-up infarct volumes, suggests that the
natural evolution of brain tissue due to ischaemic injury was
altered by the thrombolytic treatment. Because treatment
decision was not randomized, nor placebo-controlled, selection bias in patients who were given rt-PA therapy existed,
resulting in the larger mismatches and baseline NIHSSS in
the treatment group. This probably contributed to lack of
detection of significant differences using follow-up lesion
volumes since the rt-PA-treated patients had more severe
strokes initially, which led to larger lesion volumes than
those found for the non-rt-PA-treated patients. Even though
rt-PA-treated patients had follow-up lesion sizes on a
volumetric basis comparable with the non-thrombolysed
patients, it is likely that these lesion volumes were smaller
than they would have been if the patients had not received
thrombolysis, as supported by the significantly larger PLVs
compared with the actual MLVs. Our voxel-based approach,
however, was still able to detect a beneficial effect of rt-PA
even in this small patient cohort since baseline infarction
risk as captured in acute imaging is inherently incorporated
into these models.
Perhaps one of the biggest benefits of voxel-based
techniques that combine multiple MRI modalities is that
they synthesize the different patterns possible with DWI/
PWI mismatch within a single image (Jacobs et al., 2001;
Rose et al., 2001; Wu et al., 2001). As has been previously
shown by these studies and confirmed in this one, combinations of DWI and PWI can predict tissue outcome more
accurately than using the parameters separately. The spatial
distribution of GLM-predicted values may provide insight on
tissue salvageability, leading us to speculate that in addition
to using a visual mismatch between DWI/PWI for guiding
therapeutic intervention (Hacke et al., 2005; Hjort et al.,
2005), mismatch between tissue at high and intermediate
risk may augment selecting patients most likely to respond
to therapies by providing a relatively easy-to-interpret
synopsis of tissue injury in a single image. Furthermore,
the PIs appear to give further insight into degree of
salvageability.
Although development of these algorithms can be fairly
complex, involving jack-knifing and bootstrapping techniques, nonetheless, once the model coefficients have been
derived, generation of infarction risk maps are relatively
straightforward and can be performed feasibly within a few
minutes, depending on computing platform. The steps would
be as follows:
O. Wu et al.
Spatial heterogeneity of cerebral infarction risk
2391
pre-treatment GLM-predicted infarction risk reflects severity
of the initial ischaemic injury and therefore provides insight
on which patients are more likely to reperfuse or will be more
responsive to intervention. Our tools may therefore provide a
means to evaluate potential for successful thrombolytic treatment on an individual basis before treatment. This is speculation at this point since owing to the imbalance of the two
patient groups additional analysis needs to be performed on
data collected as part of a randomized, placebo-controlled,
double-blind clinical trial of novel therapies to validate the
hypothesis that predictive algorithms can be used to reduce
the number of patients needed to test the efficacy of a new
treatment. One way to do this is to perform GLM analysis on
randomly sampled balanced patient subsets of a clinical trial
powered to detect benefit by traditional end-points and
determine if similar effects can be detected using smaller
sample sizes.
Furthermore, high values of predicted infarction risk
using only DWI and PWI should not preclude rt-PA treatment with the current model since it is not specific for
irreversible tissue injury and indeed performs poorly
in cases of reversible ADC reductions. Advanced MRI
techniques, such as pH-weighted MRI (Zhou et al., 2003)
or MRI-measured oxygen extraction fraction (Lee et al.,
2003), may provide additional information to allow better
discrimination of oligaemic, and therefore potentially viable
tissue, from irreversibly infarcted tissue, which will not
recover even with successful reperfusion. Moreover, the
inclusion of non-imaging covariates that have been implicated in the success of therapeutic intervention, such as
onset time to start of treatment (Hacke et al., 2004), site
of arterial occlusion (Derex et al., 2004; Fiehler et al.,
2005), blood glucose levels, haematocrit, age and initial
NIHSS score, to name a few, into these algorithms may
further improve predictive performance. Without having
to increase the complexity of the produced risk maps, the
approach we have presented here can readily incorporate
these additional imaging and non-imaging parameters to
improve predictions of tissue outcome. Studies are under
way to incorporate non-imaging covariates (Wu et al.,
2005b).
Conclusion
In conclusion, we have shown that MRI-based algorithms
that predict the risk of infarction in ischaemic brain tissue
can be used to assess the degree of tissue injury on a voxelwise basis, which in turn can be used to provide greater
insight into stroke pathophysiology and to improve diagnosis, prognosis and management of stroke patients. We
speculate that treatment-induced tissue salvage can be
detected as discrepancies between pre-treatment infarction
risk predictions and actual tissue outcome. Thus, if the
post-treatment measured tissue outcome is significantly
improved from pre-treatment predicted destiny, it can be
inferred that the altered tissue ‘fate’ was due to altered disease
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variability of the patient data used in the training cohort, the
larger the number of patients will be required to reduce
sensitivity of the model to addition of one patient. Clearly,
11 patients are not enough to fully characterize variations
that can be seen in human stroke populations. For example,
one contributing factor to our variability is probably the early
time point involved in this study (<6 h), which has been
previously shown to be linked to greater variability in clinical
and imaging outcomes (Schellinger et al., 2001). Additional
studies are needed, involving larger number of patients
to determine the sample size necessary to stabilize the
prediction. Ideally, the patients should be stratified according
to reperfusion to increase the homogeneity of the training
data. Since prospectively one does not know if a patient will
reperfuse or not, one could make two sets of risk maps to
predict tissue outcome under scenarios of reperfusion or
no reperfusion. One could also stratify models on treatment
to predict outcome assuming different treatment strategies.
We are currently investigating models that take into account
therapy as a factor, which we speculate will also improve
prediction of tissue outcome in treated patients (Wu et al.,
2005a).
Another limitation of these algorithms, as well as of other
approaches relying on acute imaging, is that their prognosis is
based on extent of tissue injury present at scan time. Injury
due to secondary events over the course of patient care, for
example, additional damage due to hypotensive events or
fractionally lysed clots occluding distal arteries (Helgason,
1992; Dijkhuizen et al., 2001), would not be predictable by
our data-driven models. Indeed the key to the detection of
effects due to therapeutic intervention with our proposed
technique is the reliance that an effective treatment alters
disease progression, as we saw in the cases of patients who
reperfused owing to rt-PA administration. The course of
disease could also be altered naturally, such as through
spontaneous reperfusion (Furlan et al., 1996; Butcher et al.,
2005). However, if the number of cases with altered outcomes
is significantly larger in the treatment arm compared with the
placebo arm, one could conclude that the new treatment had
a significant biological effect.
For patients with early complete reperfusion, we found
that the follow-up infarction volumes were smaller than
those for those who did not demonstrate early reperfusion,
consistent with previous studies associating early reperfusion
with reduced lesion volumes (Jansen et al., 1999; Parsons
et al., 2002; Neumann-Haefelin et al., 2004; Butcher et al.,
2005). The reduced accuracy of these models in patients who
demonstrated early reperfusion is consistent with previous
reports showing poorer correlation of baseline DWI and
PWI lesion volumes with outcome lesion volumes after
recanalization (Schellinger et al., 2001). We also found
that the average GLM-predicted values for these patients at
the acute stage were also smaller than those in patients who
did not completely reperfuse. Since the average GLMpredicted values are significantly correlated with respect
to initial NIHSS score, we speculate that the level of
Brain (2006), 129, 2384–2393
2392
Brain (2006), 129, 2384–2393
progression, whether spontaneously or therapeutically
induced. Our results suggest that these algorithms are promising metrics for evaluating the effects of novel treatments
owing to increased sensitivity gained by comparing pretreatment predicted outcome with post-treatment measured
outcome.
Acknowledgements
This work was supported in part by grants from the Royal
Dutch Academy of Arts and Sciences, the Danish Medical
Research Council, Danish National Research Foundation
and the German Kompetenznetzwerk Schlaganfall sponsored
by the Bundesministerium fu¨r Bildung und Forschung (B5;
No. 01GI9902/4). We would like to acknowledge Kim
Mouridsen for statistical analysis advice and Dr Anders
Rodell for data analysis assistance.
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