PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
AJNR Am J Neuroradiol. Author manuscript; available in PMC May 1, 2013.
Published in final edited form as:
PMCID: PMC3640318
NIHMSID: NIHMS459594
Predicting language improvement in acute stroke patients presenting with aphasia: a multivariate logistic model using location-weighted atlas-based analysis of admission CT perfusion scans
Seyedmehdi Payabvash, MD,1 Shahmir Kamalian, MD,1 Steve Fung, MD,1 Yifei Wang, BSc,2 John Passanese, BA,1 Shervin Kamalian, MD,1 Leticia CS Souza,1 Andre Kemmling, MD, BSc,3 Gordon J. Harris, PhD,1 Elkan F. Halpern, PhD,1,4 R. Gilberto Gonzalez, MD PhD,1 Karen L. Furie,5 and Michael H. Lev, MD1
1Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
2Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
3Department of Radiology, University of Muenster Medical Center, Muenster, Germany
4Department of Radiology and Institute for Technology Assessment, Massachusetts General Hospital and Harvard Medical School, Boston, MA
5Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
Address correspondence and reprint request to:Michael H. Lev, MD, Massachusetts General Hospital, Department of Radiology-GRB285, PO Box 9657, Boston, MA 02114-9657, mlev/at/partners.org, (Tel): 617-724-7125, (Fax): 617-724-3338, (Cell): 857-231-1197
Purpose
To construct a multivariate model for prediction of early aphasia improvement in stroke patients using admission CT perfusion (CTP) and CT angiography (CTA).
Methods
Fifty-eight consecutive patients with aphasia due to first-time ischemic stroke of the left hemisphere were included. Language function was assessed based on patients’ admission and discharge NIHSS and clinical records. All patients had brain CTP and CTA within 9 hours of symptom onset. For image analysis, all CTPs were automatically coregistered to MNI-152 brain space and parcellated into mirrored cortical and subcortical regions. Multiple logistic regression analysis was used to find independent imaging and clinical predictors of language recovery.
Results
By the time of discharge, 21 (36%) patients demonstrated improvement of language. Independent factors predicting improvement in language included relative cerebral blood flow of angular gyrus gray matter (Brodmann’s area 39) and lower third of insular ribbon, proximal cerebral artery occlusion on admission CTA, and aphasia score on admission NIHSS exam. Using these 4 variables, we developed a multivariate logistic regression model that could estimate the probability of early improvement in stroke patients presenting with aphasia and predict functional outcome with 91% accuracy.
Conclusion
An imaging-based location weighted multivariate model is developed to predict early language improvement of aphasic patients using admission data collected within 9-hours of stroke onset. This pilot model should be validated in a larger, prospective study; however, the semi-automated atlas-based analysis of brain CTP, along with the statistical approach, could be generalized for prediction of other outcome measures in stroke patients.
Aphasia can be one of the most devastating consequences of acute stroke, present in 16–37% of patients. Those with aphasia have poor long-term functional outcome,1 reduced probability of returning to work,2 and increased mortality rate,3 compared to non-aphasic stroke patients.
The importance of predicting language recovery early in the course of stroke is highlighted by recent reports showing that brain reorganization occurs earlier than previously thought, and that early targeted post-stroke rehabilitation therapy significantly improves outcomes.4 Thus, identifying aphasic patients for early intervention could be valuable in designing an effective treatment program, including interventions such as acute thrombolytic therapy, neuroprotective agents, and acute speech therapy. Few studies, however, have provided clinicians with practical information for predicting the extent to which a patient may recover communicative abilities.
Age, aphasia type, and infarct size have been associated with long-term improvement of aphasia.5 However, it is clear that early improvement of stroke symptoms is mostly dependent on reperfusion of distressed but still viable brain tissue (penumbra). The decrease in cerebral blood flow (hypoperfusion) during stroke may result in cessation of neuronal activity without immediate cell death in areas surrounding the infarct core.6 Depending on perfusion hemodynamics, neurons in these areas may ultimately die, remain hypoperfused, or reperfuse with restoration of normal neuronal function. Prior studies have revealed a correlation between different types of aphasic syndromes and the location of hypoperfused brain.7, 8 Indeed, Hillis et al reported that MR perfusion/diffusion mismatch in Brodmann’s area 37 (BA 37) was associated with recovery of “naming” function in stroke patients.9
CT perfusion (CTP) imaging of the brain is commonly used in clinical practice to assess cerebral hemodynamics in acute stroke patients, due in part to its practicality in terms of availability, speed, and cost. In the present study, we sought to develop a location weighted multivariate model to predict early improvement of aphasia in acute stroke patients based on admission clinical and imaging findings, using a semi-automated method of CTP image analysis. Short-term recovery was evaluated in our study, as it is more related to early reperfusion brain tissue salvage compared to long-term clinical outcome that is largely affected by rehabilitation therapy, structural reorganization, and recruitment of unrelated brain areas.
Patients
We retrospectively reviewed our prospectively collected database to find patients with acute ischemic stroke admitted between December 2006 and April 2008 in our center. A total of 119 consecutive patients were identified; among these, 58 patients were included based on the following criteria: unilateral left hemispheric stroke; any signs of aphasia associated with stroke onset according to medical records; CTP within 9 hours of symptom onset; no evidence of chronic infarction; and technically usable CTP images. This study received approval from our Institutional Review Board and was compliant with the Health Insurance Portability and Accountability Act.
Aphasia symptoms of each patient were extracted from the medical records based on physical exam notes, including component NIHSS exam scores, recorded at admission and discharge. Patients were categorized into two groups: those with improvement of language function by the time of discharge and those with no clinically detectable improvement. Improvement of language function was considered when there was a decrease in the NIHSS aphasia score or an unequivocal statement in the medical record confirming the improvement of language. Patients with equivocal or inadequate data were excluded.
Patients were treated with standard therapies, including intravenous (IV) thrombolysis or intra-arterial (IA) therapy, as clinically indicated.
Image acquisition
All CT scans were performed on a 64 detector row volume CT helical scanner (Light Speed; GE Medical Systems, Milwaukee, WI). In all patients, unenhanced CT scanning was followed immediately on the same scanner by CT-angiography (CTA) and CTP imaging. CTA was performed at 120 kV, 200 mAs, slice thickness 1.25 mm, helical scanning mode, and IV administration of 65–75 mL of non-ionic iodine contrast material (Isovue Multipack 370; Bracco Diagnostics Inc., Princeton, NJ) at 4 mL per second. For each of the two anterior circulation CTP slabs studied (one with lowest slice at the level of the circle of Willis, and one immediately superior to this), a total of 40-mL contrast material was injected using a power injector at a flow rate of 7 mL/s. Five seconds after the start of injection, dynamic scanning was performed as a 66-second biphasic cine series using the following parameters: 8 contiguous 5-mm-thick sections (4 cm vertical coverage per each slab), 80 kVp, 200 mA, 1-second rotation time, 1 image per second for 40 seconds with 9 additional images one every 3rd second. The total estimated effective radiation dose to the head was 11.6 mSv (unenhanced CT of the head, 2.5 mSv; CTA of the head, 2.5 mSv; and 2-slab CTP of the head, 6.7 mSv).
The acquired CTP series were transferred to a GE Advantage workstation (General Healthcare, Milwaukee, WI) for post-processing of CTP maps including cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT), using delay-corrected deconvolution-based commercial CT perfusion software (CT Perfusion 4, General Healthcare, Milwaukee, WI); the software automatically excludes vessels based on a preset threshold. The reference arterial input function (AIF), was selected by the software in a region of interest that the user manually drew around the terminal ICA or anterior cerebral artery – care was taken not to select the AIF anywhere distal to the site of major arterial occlusions.10
Image analysis
All CTP maps were automatically co-registered to MNI-152 standard brain space using 12-parameter affine linear transformations (FLIRT version 5.5; FMRIB's Linear Image Registration Tool, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Oxford, UK).11 In most cases, the lower and upper slabs were appended to create a single 16-slice CTP map. All scans were visually controlled for satisfactory co-registration and there was no need for any further manual correction. Custom-written software (MatLab 7.8; The MathWorks, Natick, MA) was used for automated image analysis. Briefly, all CTP maps were subsegmented into paired mirror cortical and subcortical areas based on preset atlases implanted in FSL 4.1.2 (FMRIB Software Library, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Oxford, UK). We modified the Talairach and JHU atlases supplied with the FSL package and divided the large subcortical areas into smaller sub-regions for more precise evaluation of different brain areas. A total of 146 pairs of regions were automatically segmented. The average region volume was 6.4 ± 2.1 ml. Then, the relative CTP values (rCBF, rCBV, and rMTT) for each brain region were calculated as the ratio of left-hemispheric (stroke-side) mean value divided by contralateral mirror region mean value (hence, each relative CTP parameter value is defined as: [ischemic region CTP value]/[contralateral mirror region CTP value]; all values reflect the mean quantitative units from the scan region of interest).
In each patient, intracranial arterial occlusion was determined based on the admission CTA study as recorded in the prospective clinical report.12 Patients were defined to have proximal cerebral artery occlusion in case of either distal/terminal internal carotid artery (ICA), or proximal (M1 or M2) middle cerebral artery (MCA) cut off. The presence of proximal cerebral artery occlusion was a binary variable assigned as 1 in the presence of occlusion, and 0 in the absence of occlusion.
Statistical analysis
For statistical analysis, we categorized patients into two groups: those with and those without improvement of aphasia at discharge. First, we compared all clinical and imaging variables between the two groups in a univariate analysis using the Student’s t test, Wilcoxon Rank-Sum (Mann-Whitney) test, or Chi square test, as appropriate.
In order to determine which brain areas and CTP parameters were best predictive of language improvement, Receiver Operating Characteristics (ROC) curves were generated to identify those CTP parameters that could predict improvement of speech most accurately based upon the area under the curve (AUC) (larger AUC value suggests that the corresponding variable can predict improvement of speech more accurately.). For each brain region (146 pairs totally), we compared rCBF, rCBV, and rMTT values between study groups separately.
Next, a multiple logistic regression model using stepwise selection was constructed to determine which of the CTP regions were independent predictors of language improvement. Given the size of our study cohort relative to the potential number of variables in our model, only those variables that had the highest possibility for independent prediction of outcome in our logistic regression were included. Specifically, only those CTP parameters that could distinguish between the two study groups with ROC AUC > 0.75 were included in the initial calculation of stepwise regression. If, for a particular brain region, two CTP parameters (e.g. both rCBF and rMTT) had ROC AUC > 0.75, only the one with the higher area under curve was included in the stepwise logistic regression calculation. Moreover, due to incomplete coverage of the more inferior brain regions for some patients (particularly in the inferior middle cerebral artery distribution), five regions that were not scanned in 15 cases were excluded from the model; of note, Brocca’s and Wernicke’s areas were not among the regions excluded.
An additional stepwise multiple logistic regression model was constructed to determine the independent clinical and CTA variables (Table 1) predictive of language improvement. Finally, using the imaging and clinical variables determined to be independent predictors of language improvement in the above stepwise models, forced-entry multiple logistic regression was also performed to develop overall predictive models for language improvement, with ROC AUC analysis used to determine the accuracy of these models.
Table 1
Table 1
Comparison of clinical/CTA characteristics between patients with and without improvement of aphasia (univariate analysis)
The probability of improvement for each patient was computed by entering the values for the independent predictors of the final logistic regression equation into the standard statistical formula equation M1, where “v” is the unitless numerical value derived directly from entering the input variables for a given patient into the regression equation. If “v” equals zero, there is a 50% probability of language function improvement; positive values (>0) correspond to a > 50% probability of improvement, and negative values a < 50% probability.
All statistical analyses were performed using STATA 10 (STATA; Stata, College Station, TX) and SPSS 17.0 (SPSS, SPSS Inc., Chicago, IL); all values are reported as either mean ± standard error of mean (SEM), or median (range min-to-max).
A total of 58 patients (26 male, 32 female) with aphasia due to first-time left hemisphere ischemic stroke were included in our study (Figure 1). The mean age was 74±1.8 years and the median NIHSS at the time of admission was 13 (range 2 – 28). Only 16 patients received IV thrombolytic therapy within 3 hours of stroke onset; of these, 4 also underwent thrombectomy at 8 – 9 hours after stroke onset.
Figure 1
Figure 1
Of 119 patients with acute ischemic stroke who underwent CTP at our hospital between December 2006 and April 2008, 58 were included in this study.
By discharge (median: 6 days, range: 3 – 22 days), 21 (36%) of patients had clinically detectable improvement of language function. In Table 1, admission clinical characteristics, CTA findings, and time intervals are compared between patients with and without improvement of aphasia. Those with improvement had significantly lower total NIHSS score and NIHSS aphasia score at admission, compared to those without improvement. Moreover, aphasic patients who had proximal cerebral artery occlusion at admission were significantly less likely to experience any improvement of language function by discharge (Odds Ratio for language improvement 0.117; 95% CI: 0.034 to 0.397).
There was no significant difference in percentage of patients with improvement for the subgroups who received IV thrombolysis or thrombectomy. There was only one patient (out of 4) who had complete recanalization of occlusion following thrombectomy (Table 1).
Of note, the majority of the CTP scans were acquired following IV thrombolytic treatment (11/16, 69%). The ictus-to-treatment, treatment-to-scan, and ictus-to-scan time intervals were not significantly different between the two study groups (Table 1). All thrombectomies were performed approximately 8 – 9 hours after stroke onset in patients who had already received IV therapy.
The only independent predictors of language improvement based on the regional CTP logistic regression model were rCBF values in the (1) lower third of the sub-lobar insular ribbon, and (2) angular gyrus gray matter (GM), Brodmann’s area (BA) 39 (Figure 2).
Figure 2
Figure 2
Upper row: orthogonal sections of the lower third of the sub-lobar insular ribbon. Middle row: orthogonal sections passing through the angular gyrus gray matter of Brodmann’s area 39. The relative Cerebral Blood Flow (rCBF) of these two regions (more ...)
In the subsequent clinical/CTA based stepwise multiple logistic regression, the only independent predictors of language improvement, among all variables listed in Table 1, were the (1) admission NIHSS aphasia score, and (2) the presence or absence of proximal large vessel occlusion on admission CTA. (Because there was a high level of co-linearity [R> 0.5] between the overall admission total NIHSS score and the component aphasia score of the NIHSS exam, only the specific aphasia score was included in this model).
Next, an imaging based forced entry multiple logistic regression model was constructed using only the admission CTA and CTP variables that could predict language improvement independently based on the above, stepwise models (Table 2). ROC curve analysis of the resulting regression equation predicted early language improvement with 85% sensitivity, 85% specificity, and 85% accuracy (AUC = 0.89). There were 5 false positive cases (positive predictive value: 76%) and 5 false negative cases (negative predictive value: 87%).
Table 2
Table 2
Details of the forced-entry multiple logistic regression model derived only from the admission CTP/CTA imaging independent predictors of language improvement
Finally, a forced-entry multiple logistic regression model was constructed which included all 4 of the imaging and clinical variables that were independent predictors of language improvement. Table 3 shows the details of this final logistic regression equation, resulting in an arbitrary value. Based on the ROC curve analysis, a cut point of >0 for this value results in a 90% sensitivity, 91% specificity, and 91% accuracy for prediction of language improvement. This full model, which included the clinical exam data, showed a trend towards improved prediction versus the imaging model only (p=0.103). The box-plot graph confirms the significance of this model for predicting language improvement (Figure 3). With the final model, there were only 3 false negative cases (positive predictive value: 86%) and 3 false positive cases (negative predictive value: 92%). One of the 3 false negative cases was of a patient who underwent successful thrombectomy almost 9 hours after stroke onset. This patient has received IV thrombolytic 30 minutes after stroke onset, and was scanned 47 minutes after stroke onset. There was 1 false positive case who had also received IV thrombolytic therapy; the remaining 4 false positive and false negative cases had received no thrombolytic therapy.
Table 3
Table 3
Details of the forced-entry multiple logistic regression model derived from both the admission CTP/CTA imaging and the clinical independent predictors of language improvement.
Figure 3
Figure 3
(A) The receiver-operating characteristic (ROC) curve for the forced-entry multiple logistic regression model derived from the admission CTP/CTA imaging and the clinical independent predictors of language improvement (Table 3). (B) Box-plot graph shows (more ...)
Based on the coefficients of this regression equation, we have formulated a pilot, practical, eight-point “aphasia improvement score” - using the four variables listed above - to stratify patients into five potential improvement groups, ranging from “excellent” to “dismal” (Tables 4, ,5).5). Probability values were estimated only for patients in the study population with no extrapolation to other possible cases; the scoring system was further modified to best classify patients in our cohort.
Table 4
Table 4
Calculation of the 8-point “aphasia improvement score”
Table 5
Table 5
Predictive value of the “aphasia improvement score”
The ability to treat acute stroke patients with thrombolytics has increased the need for rapid detection and evaluation of acute stroke. Increasingly, newer neuroprotective agents and recovery strategies are targeting the acute patients, raising demand for diagnostic and biomarker variables to assist with patient selection. Stroke imaging can contribute significantly by identifying patients likely to recover during the early, subacute phase due to restoration of arterial blood flow, either spontaneously or following thrombolytic treatment. It is important to not only weigh the risks versus benefits of aggressive treatment to restore cerebral perfusion, but also to support decisions regarding subsequent management.4 In this study, we developed a pilot model to predict early improvement of aphasia based on admission imaging and clinical data.
This model predicted clinical outcome with high accuracy (91%) based on data collected within 9 hours of stroke onset. In summary, aphasic patients with normal, near-normal, or hyperemic rCBF of the sub-insular ribbon and angular gyrus, and without proximal cerebral artery occlusion at admission, were most likely to show improvement of language function by hospital discharge (Table 3). Moreover, using only the four independent outcome predictors of our final model – specifically, admission NIHSS aphasia score, presence or absence of a proximal large vessel intracranial occlusion, and degree of rCBF derangement in the left subinsular ribbon and left angular gyrus - we developed a practical, simple-to-apply, pilot “aphasia improvement score” for predicting the likelihood of language improvement, which has the potential to be of value in patient triage pending validation by prospective studies. Notably, the scoring system was constructed based on the regression coefficients of our cohort population, thus requiring validation in larger independent studies.
Using the logistic regression equation of the final model (Table 3), one can more precisely estimates the probability of language improvement in a given patient based on the same four variables, as explained in the methods section. The value of our model may extend beyond the first few days following stroke onset, as the severity of aphasia within the first week post-ictus is one of the most important predictors of long-term language function, accounting for 47% of the variation in the aphasia score one year after stroke.13
Interestingly, the two brain regions we found to be independent predictors of aphasia improvement are not those most commonly thought of in association with language function (Figure 2). However, left BA 39 (angular gyrus) has been reported to have a role in semantic processing,14 and hypoperfusion/infarction of this region contributes to error rate in oral naming.7 An analogous role of the insular ribbon in language function may apply. Although the brain regions identified in our study may directly contribute to language function - to some extent 7 - our results only reveal a correlation between aphasia outcome and perfusion status of these regions, and do not prove a direct association.
Of note, numerous abnormalities have been reported to be associated with insular infarction, including sympathetic activation,15 stroke-related myocardial damage,16 and positional vertigo.17 These findings, however, may be an epiphenomenon, and simply reflect the high frequency of insular damage in large MCA territory strokes. It is also plausible that the perfusion defect in the lower insular cortex reflects edematous damage or hypoperfusion of the adjacent inferior frontal gyrus Broca’s area, or the deep arcuate fasciculus.
Regardless, our findings suggest that ischemia of these two left hemisphere regions is a strong predictor of poor outcome in aphasic patients, albeit that these cerebral areas may not directly contribute to language function. Our data suggests that mean rCBF of these two regions better predicts language recovery than other cortical and sub-cortical areas known to be more directly involved in language production; therefore potentially reversible hypoperfusion (i.e. “ischemic penumbra”) of these specific regions reflects the overall perfusion status of a complex left hemispheric language neural network.
Clearly, the acute infarction volume has prognostic value; however, for optimal prediction of language outcome, it is valuable to know not only the size and degree of this “ischemic penumbra”, but also its spatial localization.9 Because the precise definition of “penumbra” remains controversial,18 and because the thresholds for distinguishing truly “ischemic” hypoperfused tissue from “benign oligemia” have not yet been validated and standardized,1921 our approach to determine the independent predictors of language improvement has been to directly model the regional, normalized perfusion parameter values with a logistic regression, rather than segmenting these regions using arbitrary threshold values that have not yet been established. Having identified specific brain regions whose rCBF values served as independent predictors of functional improvement, we then integrated this CTP data with the admission CTA and clinical parameters to produce a more accurate, optimized model.
Admission aphasia score and cerebral arterial occlusion were also independent predictors of aphasia improvement. This is in agreement with previous reports.13 Due to the very high co-linearity between the total NIHSS score and the NIHSS aphasia score, we included only the more relevant NIHSS admission aphasia score in our model, although it is noteworthy that Pedesen et al found that the inclusion of an overall admission stroke severity score, in addition to the admission aphasia quotient, incrementally improved the accuracy of their 1-year aphasia outcome prognosis.13
The application of multivariate model using imaging variables only (Table 2) could be especially valuable in circumstances when clinical exam is unavailable or unreliable, such as the presence of a language barrier, or in an obtunded patient. Admission CTA findings were also important in this regard. We categorized CTA findings, based on the Boston Acute Stroke Imaging Scale (BASIS),12 into those patients with versus without proximal large vessel cerebral artery occlusion (intracranial ICA, M1 or M2MCA). Patients with major stroke by BASIS classification are reported to have higher mortality rates, longer durations of hospital stay, and higher rates of discharge to rehabilitation.12 Based on our results, one can approximate the probability of language improvement based on the admission CTA and CTP findings alone, albeit that the addition of admission NIHSS aphasia score improves the accuracy of this prediction.
In addition to our clinical results, our methodological approach is noteworthy, in that it has the potential to be generalized to the prediction of other clinical outcomes, such as motor function. We developed an automated method for image analysis, with minimal operator-dependent bias, and highly reproducible results. All CTP images were automatically co-registered to standard MNI-152 brain space using highly robust and effective registration software (FLIRT 5.5).11 For parcellation of the brain, a pre-set atlas based on widely available Talairach and JHU atlases supplied with FSL freeware was used.22 Thus, our image analysis was totally operator-independent, representing a major advantage over the typical methodology for such studies, which relies on manual registration/overlay for segmentation of the brain.20 In addition, this tool can be used to create a same-language forum for comparison of results across different centers. This elimination of operator dependent bias underscores the predictive strength of the resulting regression models.
Among patients receiving thrombolytic therapy, there was no significant difference between those with and without language improvement. Due to the small number of cases in our cohort, we were unable to construct separate blocks for our multivariate model sub-stratified by treatment. Moreover, many patients received IV thrombolytic therapy prior to CTP acquisition. An ideal model would be able to predict the probability that a particular treatment, at a given time after stroke onset, improves clinical outcome.
The major limitation of our study was that the same cohort was used for both model development and estimation of test characteristics; in an ideal situation, different samples should be used for model development and validation to avoid bias towards an overestimation of the test-characteristics. Another important limitation of our study was the lack of patient evaluation with a specialized “language test” battery.7, 9, 13 Using a specialized battery not only provides a more reliable measure of outcome, but also provides the ability to quantitatively rate the degree of improvement.7, 9 Moreover, the correlation between specific brain regions and functional outcome cannot be assessed when voxels in those regions, due to artifact or insufficient coverage of the inferior MCA division territory, are insufficiently scanned in every patient. As noted in the results, it was for this reason that we excluded some brain regions (n=5) from our model; importantly, these exclusions did not include Brocca’s and Wernicke’s areas. Of note, , MR perfusion or CTP with 256 and 320 multi-detector CT scanners can potentially improve spatial coverage of the brain.
An additional caveat is the hypotheses relating special functional outcomes to particular regions of brain, whether one identifies areas by Brodmann’s areas, gyri, or groups of voxels on scans registered to spatial coordinates of an “idealized” brain image (MNI-152): even the most robust and effective methods of co-registration or normalization of brain images are imperfect. Each individual patient has a unique pattern of sulci, gyri, and a variable degree of structural deformity that may interfere with co-registration to MNI-152 young brain template. However, visual inspection of co-registered CTP images in our series confirmed adequate cortical co-registration. Moreover, exclusion of patients with previous ischemic stroke and inclusion of cased with acute ischemic stroke in our study minimized asymmetrical brain abnormalities that might interfere with effective co-registration.
We have presented an automated, operator-independent and reproducible method for atlas-based analysis of brain regions on CT perfusion images. Using this technique, we have developed a pilot logistic regression model that can predict the likelihood of early clinical improvement in stroke patients presenting with aphasia, based on the admission CTP and CTA data, with a high level of accuracy (85%). The accuracy of this model increased to 91% with the addition of admission clinical data (aphasia score on admission NIHSS exam) to the model. Although structural reorganization largely contributes to the long term clinical outcome of stroke patients, early brain perfusion and clinical status also play a major role in acute management - and hence long term outcome - underscoring the potential clinical utility of this pilot model, which will need to be validated in a larger, prospective cohort.
Acknowledgments
Source of funding: This work was supported by the Specialized Programs of Translational Research in Acute Stroke (SPOTRIAS) Network grant funded by the National Institute of Health (NIH) (P50 NS051343), the Agency for Healthcare Research and Quality grant AHRQ R01 HS11392, and the Massachusetts General Hospital Clinical Research Center (No. 1 UL1 RR025758-01) Harvard Clinical and Translational Science Center, from the National Center for Research Resources
Abbreviation Key
CTPCT perfusion
CTACT angiography
NIHSSNational institute of health stroke scale
MNIMontreal neuroscience institute
IVintravenous
IAintra-arterial
FOVField of view
rCBFrelative cerebral blood flow
CBVrelative cerebral blood volume
MTTrelative mean transit time
BABrodmann’s area
FLIRTFunctional-MRI Linear Image Registration Tool
FSLFunctional-MRI Software Library
ICAInternal carotid artery
MCAmiddle cerebral artery
ROCReceiver Operating Characteristics
AUCarea under the curve
GMgray matter
WMwhite matter
AIFarterial input function

1. Paolucci S, Antonucci G, Pratesi L, et al. Functional outcome in stroke inpatient rehabilitation: predicting: no, low and high response patients. Cerebrovasc Dis. 1998;8:228–234. [PubMed]
2. Black-Schaffer RM, Osberg JS. Return to work after stroke: development of a predictive model. Arch Phys Med Rehabil. 1990;71:285–290. [PubMed]
3. Laska AC, Hellblom A, Murray V, et al. Aphasia in acute stroke and relation to outcome. J Intern Med. 2001;249:413–422. [PubMed]
4. Lazar RM, Speizer AE, Festa JR, et al. Variability in language recovery after first-time stroke. J Neurol Neurosurg Psychiatry. 2008;79:530–534. [PubMed]
5. Nakagawa T, Murata Y, Kojima T, et al. Prognostic value of brain perfusion single-photon emission computed tomography (SPECT) for language recovery in patients with aphasia. Nucl Med Commun. 2005;26:919–923. [PubMed]
6. Hakimelahi R, Gonzalez RG. Neuroimaging of ischemic stroke with CT and MRI: advancing towards physiology-based diagnosis and therapy. Expert Rev Cardiovasc Ther. 2009;7:29–48. [PubMed]
7. Newhart M, Ken L, Kleinman JT, et al. Neural networks essential for naming and word comprehension. Cogn Behav Neurol. 2007;20:25–30. [PubMed]
8. Reineck LA, Agarwal S, Hillis AE. "Diffusion-clinical mismatch" is associated with potential for early recovery of aphasia. Neurology. 2005;64:828–833. [PubMed]
9. Hillis AE, Gold L, Kannan V, et al. Site of the ischemic penumbra as a predictor of potential for recovery of functions. Neurology. 2008;71:184–189. [PubMed]
10. Ferreira RM, Lev MH, Goldmakher GV, et al. Arterial Input Function Placement for Accurate CTP Map Construction in Acute Stroke. AJR Am J Roentgenol. 2010 In Press. [PubMed]
11. Jenkinson M, Bannister P, Brady M, et al. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002;17:825–841. [PubMed]
12. Torres-Mozqueda F, He J, Yeh IB, et al. An acute ischemic stroke classification instrument that includes CT or MR angiography: the Boston Acute Stroke Imaging Scale. AJNR Am J Neuroradiol. 2008;29:1111–1117. [PubMed]
13. Pedersen PM, Vinter K, Olsen TS. Aphasia after stroke: type, severity and prognosis. The Copenhagen aphasia study. Cerebrovasc Dis. 2004;17:35–43. [PubMed]
14. Binder JR, McKiernan KA, Parsons ME, et al. Neural correlates of lexical access during visual word recognition. J Cogn Neurosci. 2003;15:372–393. [PubMed]
15. Sander D, Klingelhofer J. Changes of circadian blood pressure patterns and cardiovascular parameters indicate lateralization of sympathetic activation following hemispheric brain infarction. J Neurol. 1995;242:313–318. [PubMed]
16. Ay H, Koroshetz WJ, Benner T, et al. Neuroanatomic correlates of stroke-related myocardial injury. Neurology. 2006;66:1325–1329. [PubMed]
17. Brandt T, Botzel K, Yousry T, et al. Rotational vertigo in embolic stroke of the vestibular and auditory cortices. Neurology. 1995;45:42–44. [PubMed]
18. Olivot JM, Mlynash M, Thijs VN, et al. Optimal Tmax threshold for predicting penumbral tissue in acute stroke. Stroke. 2009;40:469–475. [PMC free article] [PubMed]
19. Wintermark M, Flanders AE, Velthuis B, et al. Perfusion-CT assessment of infarct core and penumbra: receiver operating characteristic curve analysis in 130 patients suspected of acute hemispheric stroke. Stroke. 2006;37:979–985. [PubMed]
20. Schaefer PW, Roccatagliata L, Ledezma C, et al. First-pass quantitative CT perfusion identifies thresholds for salvageable penumbra in acute stroke patients treated with intra-arterial therapy. AJNR Am J Neuroradiol. 2006;27:20–25. [PubMed]
21. Wintermark M, Albers GW, Alexandrov AV, et al. Acute stroke imaging research roadmap. Stroke. 2008;39:1621–1628. [PubMed]
22. Wakana S, Caprihan A, Panzenboeck MM, et al. Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage. 2007;36:630–644. [PMC free article] [PubMed]