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Evaluation of white matter hypodensities on computed tomography in stroke patients using the Fazekas score
Clinical Imaging 46 (2017) 24–27
Contents lists available at ScienceDirect
Clinical Imaging journal homepage: http://www.clinicalimaging.org
Evaluation of white matter hypodensities on computed tomography in stroke patients using the Fazekas score Salvatore Rudilosso a, Luis San Román b, Jordi Blasco b, María Hernández-Pérez c, Xabier Urra a,⁎, Ángel Chamorro a a b c
Functional Unit of Cerebrovascular Diseases, Hospital Clinic, Barcelona, Spain Department of Radiology, Hospital Clinic, Barcelona, Spain Department of Neuroscience, Hospital Germans Trias i Pujol, Universitat Autònoma de Barcelona, Barcelona, Spain
a r t i c l e
i n f o
Article history: Received 28 December 2016 Received in revised form 26 May 2017 Accepted 28 June 2017 Available online xxxx Keywords: Fazekas score White matter changes Stroke
a b s t r a c t Purpose: To assess the reliability of the Fazekas score on brain CT in acute stroke patients. Methods: Two raters evaluated the Fazekas score in 157 CT scans from consecutive patients with acute stroke. Results: The Fazekas scores on brain CT scans showed consistent (weighted κ, 0.73) and moderate (weighted κ, 0.56) interobserver agreement for periventricular and deep white matter areas, respectively. Intraobserver reliability was substantial for both areas (weighted κ, 0.85 and 0.8). Conclusion: The Fazekas score on CT can be used to reliably grade white matter changes, and can be a useful tool when MRI is not available. © 2017 Elsevier Inc. All rights reserved.
1. Introduction Small vessel disease is the most prevalent age-related pathological process affecting the cerebral microcirculation and leads to a range of different parenchymal lesions that play a central role in the development of severe neurological disorders such as dementia, gait unbalance and depression [1]. Among the radiological manifestations of small vessel disease, the most frequent findings are symmetrical subcortical lesions defined as white matter changes (WMC) of presumed vascular origin [2]. WMC are visible as hyperintensities on T2-weighted or FLAIR MRI sequences and as white matter hypoattenuations or hypodensities on computed tomography (CT). WMC are strongly associated with vascular risk factors and cerebrovascular disease [1], and WMC have been related to poor functional recovery after ischemic stroke [3,4] and to intracranial hemorrhage after iv thrombolysis [5]. Many radiological qualitative and quantitative scales are available to characterize WMC on neuroimaging. Some automatic or semiautomatic methods that allow a quantitative analysis of WMC are used mainly in research, whereas visual rating scores are more commonly used in clinical practice. Few of these visual scales are applicable for both MRI and CT brain imaging [6]. The Fazekas scale is the best studied and widely used scale for MRI and allows grading WMC in both periventricular (PV) and deep white matter (DWM) areas [7]. This scale has been validated histopathologically and is easy to perform, enabling a quantitative and qualitative analysis of WMC with good inter and intraobserver ⁎ Corresponding author at: Hospital Clínic, 170 Villarroel, 08036 Barcelona, Spain. E-mail address: [email protected] (X. Urra).
http://dx.doi.org/10.1016/j.clinimag.2017.06.011 0899-7071/© 2017 Elsevier Inc. All rights reserved.
reliability [6]. Despite the higher definition and accuracy of MRI, brain CT remains in most centers the fastest and most available technique for the assessment of patients with acute ischemic stroke when reperfusion therapy is considered. For this reason, the Fazekas score has already been used on CT scans for the evaluation of patients with acute stroke [8, 9] although it was originally conceived to be used on MRI. In this study, we evaluated the usefulness of the Fazekas scale on CT scans in patients with acute stroke. To do so, we first evaluated the interobserver and intraobserver agreement in the grading of WMC on CT scans using the Fazekas score, and then we assessed the diagnostic yield of the technique compared to MRI.
2. Materials and methods 2.1. Patients This is a single-center, retrospective, observational study. We screened our stroke registry from November 2008 to January 2015 and selected patients with acute ischemic stroke due to a proximal arterial occlusion treated with mechanical thrombectomy (MT) who had both a baseline CT scan and a follow-up MRI to ensure a clinically and radiologically homogeneous population. In order to evaluate the relevance of the Fazekas scale on CT scans in clinical practice, poor imaging quality of the scans was not considered an exclusion criterion for this study, as movement artifacts in patients with stroke are frequent. On the contrary, patients with incomplete or poorly defined MRI scans were not analyzed.
S. Rudilosso et al. / Clinical Imaging 46 (2017) 24–27
2.2. Image acquisition In our institution, non-enhanced brain CT is the neuroimaging study performed to rule out hemorrhagic stroke or malignant ischemic stroke while CT angiography is used to assess vascular occlusions. Both studies were performed simultaneously on a Somatom Definition Flash 128section dual-source CT system (Siemens, Erlangen, Germany). MRI studies were performed on a 1.5 T scanner. The stroke MRI protocol included T2 FLAIR and DWI sequences, obtained with 5-mm section thickness.
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already available before the design of this study in the registry of patients with stroke treated with MT, and had been evaluated by one of the raters of this study (SR). In many clinical studies the WMC grades from complex WMC scales were analyzed in a dichotomized fashion [3,4,10,11,12,13,14,15,16,17, 18]. We used one of the commonest dichotomizations to classify the Fazekas scores obtained from both CT scans and MRI into low (grade 0 and 1 in Fazekas score) and high (grade 1 and 2 in Fazekas score) WMC load in periventricular and deep areas separately [10,12,14,17]. 2.4. Statistics
2.3. Image analysis The CT scans were evaluated by an expert neuroradiologist (LS) and a trained neurologist (SR), blinded to MRI and clinical data. Acute strokes can appear as hypoattenuations on CT scans. For this reason, when asymmetric subcortical hypodensities were observed, only the less affected hemisphere was evaluated. Periventricular (PV) and deep white matter (DWM) hypodensities were scored separately using the four-point scale according to the Fazekas score on MRI [7]. PV score was: 0 (absence), 1 (caps or pencil-thin lining), 2 (smooth halo) or 3 (irregular PV lesions extending into the deep white matter). DWM score was: 0 (absence), 1 (punctate foci), 2 (beginning confluence of foci) or 3 (large confluent areas) (Fig.1). The Fazekas scores on MRI were
Interobserver and intraobserver agreement for Fazekas scores on CT scans were assessed using the Cohen's weighted κ statistic [19]. κ values were defined following the Altman methodology [20]. To assess intraobserver reliability, one of the two readers (SR) evaluated the CT scans twice, with an interval of 15 days between the readings. The agreement between the Fazekas scores on CT and MRI was assessed with weighted κ. Finally, the Fazekas scores on MRI were used as gold standard to assess the accuracy of the Fazekas scale on CT. Thus, we calculated sensitivity and specificity for high WMC load (Fazekas grades 2 and 3) using the first reading on CT scans of one of the two raters (SR). Normally distributed continuous variables were described as means and standard deviations and group differences were tested using t-test.
Fig. 1. Rating of the Fazekas score. Examples of 0, 1, 2 and 3 scores in CT and MRI scans for PV areas and DWM areas. Each pair of images (CT/MRI) corresponds to the same patient. The circles indicate hypodensities on CT and hyperintensities on FLAIR in the hemisphere opposite the acute ischemic stroke.
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S. Rudilosso et al. / Clinical Imaging 46 (2017) 24–27
Categorical variables were presented as percentages and group analyses were conducted using χ2 or Fisher exact test. Two-sided significance tests were used throughout, and a 2-sided p b 0.05 was considered statically significant. All statistical analyses were performed using SPSS Statistics 20 (IBM, Armonk, NY). 3. Results A total of 1133 patients were screened during the enrolling period and 157 finally met the inclusion criteria (Fig.2). The main clinical characteristics of the patients are summarized in Table 1. The median delay from stroke onset to baseline CT scan was 99 min and to follow-up MRI 39.7 h. The quality of CT scans was good in 94.3% of all cases. High WMC load for both PV and DWM areas was significantly associated with age and atrial fibrillation, while systolic blood pressure at admission was highest in patients with high WMC load in PV areas (Table 2). These associations were almost identical with the WMC load on MRI (data not shown). The interobserver agreement of the Fazekas score on CT was consistent (κ = 0.73, 95%CI [0.61–0.84]) for PV areas, and moderate (κ = 0.56, 95%CI [0.38–0.74]) for DWM areas. Intraobserver reliability was almost perfect for both PV and DWM areas (κ = 0.85, 95%CI [0.78–0.92] and κ = 0.83 [0.73–0.92] respectively). On MRI, the Fazekas scale score for PV areas was 0 in 23.6%, 1 in 43.3%, 2 in 27.4% and 3 in 5.7% of cases. For DWM areas, Fazekas score on MRI was 0 in 51.6%, 1 in 34.4%, 2 in 10.8% and 3 in 3.2% of cases. The WMC load score on MRI was high in 33.1% of the cases in PV areas, and in 14.0% of the cases in DWM areas. The agreement between the Fazekas scores on CT and MRI was consistent for both PV (κ = 0.66, 95%CI [0.51–0.81]) and DWM (κ = 0.62, 95%CI [0.48–0.77]) areas. The sensitivity for high WMC load was 67.3% for PV areas and 86.4% for DWM areas, the specificity was 91.4% for PV areas and 88.9% for DWM areas, and the predictive value for high WMC load was 79.5% for PV areas and 55.9% for DWM areas. 4. Discussion The main result of the present study is that grading WMC in a widely available technique as CT is reliable and provides similar results compared with MRI. The grading of WMC with the Fazekas score on CT scans had good interobserver and intraobserver agreement, particularly for PV areas. A more limited ability of CT scans in distinguishing scattered and poorly defined hypodensities from normal white matter explains the lower performance of the scale to grade DWM areas. Still, the fact that the Fazekas scale evaluates separately PV and DWM areas
Fig. 2. Flow chart of the selection of the present cohort of patients.
Table 1 General characteristics of the study population. Age, years, mean (SD) Female sex, n (%) Hypertension, n (%) Diabetes mellitus, n (%) Dyslipidemia, n (%) Atrial fibrillation, n (%) Coronary artery disease, n (%) Previous stroke, n (%) Baseline mRS, median (IQR) Baseline NIHSS, median (IQR) Side of the stroke, n (%) Left Right Vascular territory of stroke, n (%) Anterior circulation Posterior circulation TOAST, n (%) Large-artery atherosclerosis Small vessel disease Cardioembolic Stroke of undetermined cause Stroke of other determined cause TICI 2b-3 score, n (%) Symptomatic ICH, n (%) Good outcome at 90 days, n (%)
65.9 (13.8) 76 (48.4) 92 (58.6) 28 (17.8) 61 (38.9) 56 (35.7) 27 (17.2) 22 (14.0) 0 (0–1) 16 (12−21) 83 (52.9) 59 (37.6) 135 (86) 20 (12) 24 (15.3) 0 (0) 70 (44.6) 48 (30.6) 15 (9.6) 128 (81.5) 8 (5.1) 108 (68.8)
NIHSS: National Institute of Health Stroke Scale; TOAST (Trial of Org 10172 in Acute Stroke Treatment); TICI (Thrombolysis in Cerebral Infarction); ICH (Intracranial Hemorrhage).
is of interest since their pathophysiological significance might be different. This distinction in the WMC grading is suitable for studies looking at the clinical significance of different radiological manifestations of cerebral small vessel disease. In this cohort, high WMC load on CT scan was related with clinical variables that are known to be associated with small vessel disease, such as older age and higher systolic blood pressure at admission, suggesting that the grading in CT scans does reflect brain lesions associated with chronic microvascular damage. An ideal WMC rating scale would be easy to perform both in CT and MRI scans. To our knowledge only 2 visual rating scales for WMC have been validated for both techniques [6,21]: the first one is the van Swieten scale, which is a three-point scale that grades WMC in anterior and posterior PV areas separately. This scale has a good reliability (weighted κ value 0.63 and 0.78 for CT and MRI respectively), but the agreement between the evaluation of this scale on CT and on MRI was not assessed [22]. The second validated score is the Age-Related White Matter Changes (ARWMC) designed by Wahlund et al., which grades 5 different cerebral territories but does not differentiate PV from DWM areas [23]. This scale showed a moderate interrater reliability for CT (κ = 0.48) and the sensitivity in extensive WMC lesions was similar on both CT and MRI, but it's main limitation is that it is complex for untrained physicians. Alternatively, the Fazekas scale allows a simple and global assessment of WMC, whereby it has been already employed in many clinical studies [4,18,24–26]. Although the accuracy of the scale on CT is inferior compared to MRI, especially for DWM areas, the Fazekas scale on CT is a reasonable alternative to brain MRI to grade WMC. This is relevant for patients with stroke whose early diagnosis and management is often based in brain CT. For example, severe WMC has been related to the risk of hemorrhagic complications after thrombolysis, [5] and the Fazekas score on CT could therefore be of interest in registries and studies analyzing the impact of white matter disease on the safety of revascularization or other acute therapies of stroke. Our study has several limitations. First, the external validity might be weakened by our selection criteria. Our cohort is younger and has less grade of small vessel disease compared to other cohorts of stroke patients, and the inclusion of patients with less WMC load may have underestimated the value of the Fazekas score on CT because the agreement between CT and MRI was better in patients with greater scores.
S. Rudilosso et al. / Clinical Imaging 46 (2017) 24–27
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Table 2 Associations between the dichotomized Fazekas score on CT scans and clinical data.
Age, (SD) Female sex n, (%) Hypertension, n (%) Diabetes mellitus, n (%) Dyslipidemia, n (%) Atrial fibrillation, n (%) SBP at admission, mmHg (SD) Glucose at admission, mg/dL (SD) NIHSS at admission, median (IQR) mRS 90 days, median (IQR) Good outcome, n (%) Symptomatic ICH, n (%) DWI volume, mean (SD)
Low PV load WMC N = 113 (72%)
High PV load WMC N = 44 (28%)
p
Low DWM load WMC N = 123 (78%)
High DWM load WMC N = 34 (22%)
p
62.8 (14.2) 50 (44.2) 61 (54) 18 (15.9) 41 (36.3) 35 (31) 143.4 (25.7) 131.0 (53.2) 15 (11−21) 2 (1–4) 82 (72.6) 6 (5.3) 45.5 (53.3)
74.1 (8.3) 26 (59.1) 31 (70) 10 (22.7) 20 (45.5) 21 (47.7) 153.4 (26.2) 134.3 (39.8) 16.5 (12–21) 3 (2–4) 26 (59.1) 2 (4.5) 33.2 (40.1)
b0.001 0.095 0.060 0.318 0.29 0.049 0.038 0.680 0.864 0.467 0.102 1 0.122
63.6 (14.2) 58 (47.2) 68 (55.3) 20 (16.3) 45 (36.6) 37 (30.1) 144.1 (25.9) 129.7 (52.0) 16.9 (12–21) 2 (1–4) 87 (70.7) 6 (4.9) 43.4 (51.7)
74.4 (7.5) 18 (52.9) 24 (70.6) 8 (23.5) 16 (47.1) 19 (55.9) 154 (26.2) 140.0 (41.0) 16 (11–21) 2.5 (2–4) 21 (61.8) 2 (5.9) 37.3 (44.2)
b0.001 0.550 0.109 0.327 0.267 0.005 0.059 0.227 0.920 0.788 0.318 0.684 0.505
SBP: systolic blood pressure; DWI: diffusion-weighted image; NIHSS: National Institute of Health Stroke Scale; mRS: modified Rankin scale; good outcome: mRS b 3 at 90 days.
Finally, the agreement of the Fazekas score on MRI was not assessed in this study, because its value has been already established previously [7]. 5. Conclusions The grading of WMC on CT scans with the Fazekas scale enables a reliable global evaluation of both PV and DWM areas. Even if MRI is still the best technique for evaluating WMC, the Fazekas score on CT is a simple way of assessing small vessel disease in stroke patients, especially in the acute phase or when MRI is not available. The Fazekas score on CT may also be useful in research studies involving large cohorts of stroke patients evaluated with CT. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Acknowledgments None. References [1] Pantoni L. Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges. Lancet Neurol 2010;9:689–701. [2] Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 2013;12(8):822–38. http://dx.doi.org/10. 1016/S1474-4422(13)70124-8. [3] Henninger N, Lin E, Baker SP, Wakhloo AK, Takhtani D, Moonis M. Leukoaraiosis predicts poor 90-day outcome after acute large cerebral artery occlusion. Cerebrovasc Dis 2012;33(6):525–31. http://dx.doi.org/10.1159/000337335. [4] Kim GM, Park KY, Avery R, Helenius J, Rost N, Rosand J, et al. Extensive leukoaraiosis is associated with high early risk of recurrence after ischemic stroke. Stroke 2014; 45(2):479–85. http://dx.doi.org/10.1161/STROKEAHA.113.003004. [5] Palumbo V, Boulanger JM, Hill MD, Inzitari D, Buchan AM. Leukoaraiosis and intracerebral hemorrhage after thrombolysis in acute stroke. Neurology 2007;68(13): 1020–4. [6] Scheltens P, Erkinjunti T, Leys D, Wahlund LO, Inzitari D, del Ser T, et al. White matter changes on CT and MRI: an overview of visual rating scales. European Task Force on Age-related White Matter Changes. Eur Neurol 1998;39(2):80–9. [7] Fazekas F, Chawluk JB, Alavi A, Hurting HI, Zimmerman RA. MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. Am J Neuroradiol 1987;8:421–6. [8] Bentley P, Ganesalingam J, Carlton Jones AL, Mahady K, Epton S, Rinne P, et al. Prediction of stroke thrombolysis outcome using CT brain machine learning. Neuroimage Clin 2014;4:635–40. http://dx.doi.org/10.1016/j.nicl.2014.02.003.
[9] Sanossian N, Ovbiagele B, Saver JL, Alger JR, Starkman S, Kim D, et al. Leukoaraiosis and collaterals in acute ischemic stroke. J Neuroimaging 2011;21(3):232–5. http:// dx.doi.org/10.1111/j.1552-6569.2010.00512.x. [10] Kang HJ, Stewart R, Park MS, Bae KY, Kim SW, Kim JM, et al. White matter hyperintensities and functional outcomes at 2 weeks and 1 year after stroke. Cerebrovasc Dis 2013;35(2):138–45. http://dx.doi.org/10.1159/000346604. [11] Bahrainwala ZS, Hillis AE, Dearborn J, Gottesman RF. Neglect performance in acute stroke is related to severity of white matter hyperintensities. Cerebrovasc Dis 2014;37(3):223–30. http://dx.doi.org/10.1159/000357661. [12] Traylor M, Anderson CD, Hurford R, Bevan S, Markus HS. Oxidative phosphorylation and lacunar stroke: genome-wide enrichment analysis of common variants. Neurology 2016;86(2):141–5. http://dx.doi.org/10.1212/WNL.0000000000002260. [13] Nam KW, Kwon HM, Lim JS, Lee YS. Leukoaraiosis is associated with pneumonia after acute ischemic stroke. BMC Neurol 2017;17(1):51. http://dx.doi.org/10.1186/ s12883-017-0830-5. [14] Kongbunkiat K, Wilson D, Kasemsap N, Tiamkao S, Jichi F, Palumbo V, et al. Leukoaraiosis, intracerebral hemorrhage, and functional outcome after acute stroke thrombolysis. Neurology 2017;88(7):638–45. http://dx.doi.org/10.1212/WNL. 0000000000003605. [15] Gilberti N, Gamba M, Premi E, Costa A, Vergani V, Delrio I, et al. Leukoaraiosis is a predictor of futile recanalization in acute ischemic stroke. J Neurol 2017;264(3): 448–52. http://dx.doi.org/10.1007/s00415-016-8366-y. [16] Patti J, Helenius J, Puri AS, Henninger N. White matter hyperintensity-adjusted critical infarct thresholds to predict a favorable 90-day outcome. Stroke 2016;47(10): 2526–33. http://dx.doi.org/10.1161/STROKEAHA.116.013982. [17] Traylor M, Rutten-Jacobs LC, Thijs V, Holliday EG, Levi C, Bevan S, et al. Genetic associations with white matter hyperintensities confer risk of lacunar stroke. Stroke 2016;47(5):1174–9. http://dx.doi.org/10.1161/STROKEAHA.115.011625. [18] Helenius J, Goddeau Jr RP, Moonis M, Henninger N. Impact of leukoaraiosis burden on hemispheric lateralization of the National Institutes of Health Stroke Scale deficit in acute ischemic stroke. Stroke 2016;47(1):24–30. http://dx.doi.org/10.1161/ STROKEAHA.115.011771. [19] Cohen J. Weighted kappa. Nominal scale agreement with provision for scaled disagreement or partial credit. Psychol Bull 1968;70:213–20. [20] Altman DG, Bland JM. Practical statistics for medical research. London: Chapman and Hall; 1991. [21] Fazekas F, Barkhof F, Wahlund LO, Pantoni L, Erkinjuntti T, Scheltens P, et al. CT and MRI rating of white matter lesions. Cerebrovasc Dis 2002;13(Suppl. 2):31–6. [22] van Swieten JC, Hijdra A, Koudstaal PJ, van Gijn J. Grading white matter lesions on CT and MRI: a simple scale. JNNP 1990;53:1080–3. [23] Wahlund LO, Barkhof F, Fazekas F, Bronge L, Augustin M, Sjögren M, et al. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke 2001;32(6):1318–22. [24] Kwon HM, Lynn MJ, Turan TN, Derdeyn CP, Fiorella D, Lane BF, et al. Frequency, risk factors, and outcome of coexistent small vessel disease and intracranial arterial stenosis: results from the stenting and aggressive medical management for preventing recurrent stroke in intracranial stenosis (SAMMPRIS) trial. JAMA Neurol 2016;73(1): 36–42. http://dx.doi.org/10.1001/jamaneurol.2015.3145. [25] Helenius J, Henninger N. Leukoaraiosis burden significantly modulates the association between infarct volume and National Institutes of Health Stroke Scale in ischemic stroke. Stroke 2015;46(7):1857–63. http://dx.doi.org/10.1161/STROKEAHA.115. 009258. [26] van der Flier WM, van Straaten EC, Barkhof F, Verdelho A, Madureira S, Pantoni L, et al. Small vessel disease and general cognitive function in nondisabled elderly: the LADIS study. Stroke 2005 Oct;36(10):2116–20.
Contents lists available at ScienceDirect
Clinical Imaging journal homepage: http://www.clinicalimaging.org
Evaluation of white matter hypodensities on computed tomography in stroke patients using the Fazekas score Salvatore Rudilosso a, Luis San Román b, Jordi Blasco b, María Hernández-Pérez c, Xabier Urra a,⁎, Ángel Chamorro a a b c
Functional Unit of Cerebrovascular Diseases, Hospital Clinic, Barcelona, Spain Department of Radiology, Hospital Clinic, Barcelona, Spain Department of Neuroscience, Hospital Germans Trias i Pujol, Universitat Autònoma de Barcelona, Barcelona, Spain
a r t i c l e
i n f o
Article history: Received 28 December 2016 Received in revised form 26 May 2017 Accepted 28 June 2017 Available online xxxx Keywords: Fazekas score White matter changes Stroke
a b s t r a c t Purpose: To assess the reliability of the Fazekas score on brain CT in acute stroke patients. Methods: Two raters evaluated the Fazekas score in 157 CT scans from consecutive patients with acute stroke. Results: The Fazekas scores on brain CT scans showed consistent (weighted κ, 0.73) and moderate (weighted κ, 0.56) interobserver agreement for periventricular and deep white matter areas, respectively. Intraobserver reliability was substantial for both areas (weighted κ, 0.85 and 0.8). Conclusion: The Fazekas score on CT can be used to reliably grade white matter changes, and can be a useful tool when MRI is not available. © 2017 Elsevier Inc. All rights reserved.
1. Introduction Small vessel disease is the most prevalent age-related pathological process affecting the cerebral microcirculation and leads to a range of different parenchymal lesions that play a central role in the development of severe neurological disorders such as dementia, gait unbalance and depression [1]. Among the radiological manifestations of small vessel disease, the most frequent findings are symmetrical subcortical lesions defined as white matter changes (WMC) of presumed vascular origin [2]. WMC are visible as hyperintensities on T2-weighted or FLAIR MRI sequences and as white matter hypoattenuations or hypodensities on computed tomography (CT). WMC are strongly associated with vascular risk factors and cerebrovascular disease [1], and WMC have been related to poor functional recovery after ischemic stroke [3,4] and to intracranial hemorrhage after iv thrombolysis [5]. Many radiological qualitative and quantitative scales are available to characterize WMC on neuroimaging. Some automatic or semiautomatic methods that allow a quantitative analysis of WMC are used mainly in research, whereas visual rating scores are more commonly used in clinical practice. Few of these visual scales are applicable for both MRI and CT brain imaging [6]. The Fazekas scale is the best studied and widely used scale for MRI and allows grading WMC in both periventricular (PV) and deep white matter (DWM) areas [7]. This scale has been validated histopathologically and is easy to perform, enabling a quantitative and qualitative analysis of WMC with good inter and intraobserver ⁎ Corresponding author at: Hospital Clínic, 170 Villarroel, 08036 Barcelona, Spain. E-mail address: [email protected] (X. Urra).
http://dx.doi.org/10.1016/j.clinimag.2017.06.011 0899-7071/© 2017 Elsevier Inc. All rights reserved.
reliability [6]. Despite the higher definition and accuracy of MRI, brain CT remains in most centers the fastest and most available technique for the assessment of patients with acute ischemic stroke when reperfusion therapy is considered. For this reason, the Fazekas score has already been used on CT scans for the evaluation of patients with acute stroke [8, 9] although it was originally conceived to be used on MRI. In this study, we evaluated the usefulness of the Fazekas scale on CT scans in patients with acute stroke. To do so, we first evaluated the interobserver and intraobserver agreement in the grading of WMC on CT scans using the Fazekas score, and then we assessed the diagnostic yield of the technique compared to MRI.
2. Materials and methods 2.1. Patients This is a single-center, retrospective, observational study. We screened our stroke registry from November 2008 to January 2015 and selected patients with acute ischemic stroke due to a proximal arterial occlusion treated with mechanical thrombectomy (MT) who had both a baseline CT scan and a follow-up MRI to ensure a clinically and radiologically homogeneous population. In order to evaluate the relevance of the Fazekas scale on CT scans in clinical practice, poor imaging quality of the scans was not considered an exclusion criterion for this study, as movement artifacts in patients with stroke are frequent. On the contrary, patients with incomplete or poorly defined MRI scans were not analyzed.
S. Rudilosso et al. / Clinical Imaging 46 (2017) 24–27
2.2. Image acquisition In our institution, non-enhanced brain CT is the neuroimaging study performed to rule out hemorrhagic stroke or malignant ischemic stroke while CT angiography is used to assess vascular occlusions. Both studies were performed simultaneously on a Somatom Definition Flash 128section dual-source CT system (Siemens, Erlangen, Germany). MRI studies were performed on a 1.5 T scanner. The stroke MRI protocol included T2 FLAIR and DWI sequences, obtained with 5-mm section thickness.
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already available before the design of this study in the registry of patients with stroke treated with MT, and had been evaluated by one of the raters of this study (SR). In many clinical studies the WMC grades from complex WMC scales were analyzed in a dichotomized fashion [3,4,10,11,12,13,14,15,16,17, 18]. We used one of the commonest dichotomizations to classify the Fazekas scores obtained from both CT scans and MRI into low (grade 0 and 1 in Fazekas score) and high (grade 1 and 2 in Fazekas score) WMC load in periventricular and deep areas separately [10,12,14,17]. 2.4. Statistics
2.3. Image analysis The CT scans were evaluated by an expert neuroradiologist (LS) and a trained neurologist (SR), blinded to MRI and clinical data. Acute strokes can appear as hypoattenuations on CT scans. For this reason, when asymmetric subcortical hypodensities were observed, only the less affected hemisphere was evaluated. Periventricular (PV) and deep white matter (DWM) hypodensities were scored separately using the four-point scale according to the Fazekas score on MRI [7]. PV score was: 0 (absence), 1 (caps or pencil-thin lining), 2 (smooth halo) or 3 (irregular PV lesions extending into the deep white matter). DWM score was: 0 (absence), 1 (punctate foci), 2 (beginning confluence of foci) or 3 (large confluent areas) (Fig.1). The Fazekas scores on MRI were
Interobserver and intraobserver agreement for Fazekas scores on CT scans were assessed using the Cohen's weighted κ statistic [19]. κ values were defined following the Altman methodology [20]. To assess intraobserver reliability, one of the two readers (SR) evaluated the CT scans twice, with an interval of 15 days between the readings. The agreement between the Fazekas scores on CT and MRI was assessed with weighted κ. Finally, the Fazekas scores on MRI were used as gold standard to assess the accuracy of the Fazekas scale on CT. Thus, we calculated sensitivity and specificity for high WMC load (Fazekas grades 2 and 3) using the first reading on CT scans of one of the two raters (SR). Normally distributed continuous variables were described as means and standard deviations and group differences were tested using t-test.
Fig. 1. Rating of the Fazekas score. Examples of 0, 1, 2 and 3 scores in CT and MRI scans for PV areas and DWM areas. Each pair of images (CT/MRI) corresponds to the same patient. The circles indicate hypodensities on CT and hyperintensities on FLAIR in the hemisphere opposite the acute ischemic stroke.
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S. Rudilosso et al. / Clinical Imaging 46 (2017) 24–27
Categorical variables were presented as percentages and group analyses were conducted using χ2 or Fisher exact test. Two-sided significance tests were used throughout, and a 2-sided p b 0.05 was considered statically significant. All statistical analyses were performed using SPSS Statistics 20 (IBM, Armonk, NY). 3. Results A total of 1133 patients were screened during the enrolling period and 157 finally met the inclusion criteria (Fig.2). The main clinical characteristics of the patients are summarized in Table 1. The median delay from stroke onset to baseline CT scan was 99 min and to follow-up MRI 39.7 h. The quality of CT scans was good in 94.3% of all cases. High WMC load for both PV and DWM areas was significantly associated with age and atrial fibrillation, while systolic blood pressure at admission was highest in patients with high WMC load in PV areas (Table 2). These associations were almost identical with the WMC load on MRI (data not shown). The interobserver agreement of the Fazekas score on CT was consistent (κ = 0.73, 95%CI [0.61–0.84]) for PV areas, and moderate (κ = 0.56, 95%CI [0.38–0.74]) for DWM areas. Intraobserver reliability was almost perfect for both PV and DWM areas (κ = 0.85, 95%CI [0.78–0.92] and κ = 0.83 [0.73–0.92] respectively). On MRI, the Fazekas scale score for PV areas was 0 in 23.6%, 1 in 43.3%, 2 in 27.4% and 3 in 5.7% of cases. For DWM areas, Fazekas score on MRI was 0 in 51.6%, 1 in 34.4%, 2 in 10.8% and 3 in 3.2% of cases. The WMC load score on MRI was high in 33.1% of the cases in PV areas, and in 14.0% of the cases in DWM areas. The agreement between the Fazekas scores on CT and MRI was consistent for both PV (κ = 0.66, 95%CI [0.51–0.81]) and DWM (κ = 0.62, 95%CI [0.48–0.77]) areas. The sensitivity for high WMC load was 67.3% for PV areas and 86.4% for DWM areas, the specificity was 91.4% for PV areas and 88.9% for DWM areas, and the predictive value for high WMC load was 79.5% for PV areas and 55.9% for DWM areas. 4. Discussion The main result of the present study is that grading WMC in a widely available technique as CT is reliable and provides similar results compared with MRI. The grading of WMC with the Fazekas score on CT scans had good interobserver and intraobserver agreement, particularly for PV areas. A more limited ability of CT scans in distinguishing scattered and poorly defined hypodensities from normal white matter explains the lower performance of the scale to grade DWM areas. Still, the fact that the Fazekas scale evaluates separately PV and DWM areas
Fig. 2. Flow chart of the selection of the present cohort of patients.
Table 1 General characteristics of the study population. Age, years, mean (SD) Female sex, n (%) Hypertension, n (%) Diabetes mellitus, n (%) Dyslipidemia, n (%) Atrial fibrillation, n (%) Coronary artery disease, n (%) Previous stroke, n (%) Baseline mRS, median (IQR) Baseline NIHSS, median (IQR) Side of the stroke, n (%) Left Right Vascular territory of stroke, n (%) Anterior circulation Posterior circulation TOAST, n (%) Large-artery atherosclerosis Small vessel disease Cardioembolic Stroke of undetermined cause Stroke of other determined cause TICI 2b-3 score, n (%) Symptomatic ICH, n (%) Good outcome at 90 days, n (%)
65.9 (13.8) 76 (48.4) 92 (58.6) 28 (17.8) 61 (38.9) 56 (35.7) 27 (17.2) 22 (14.0) 0 (0–1) 16 (12−21) 83 (52.9) 59 (37.6) 135 (86) 20 (12) 24 (15.3) 0 (0) 70 (44.6) 48 (30.6) 15 (9.6) 128 (81.5) 8 (5.1) 108 (68.8)
NIHSS: National Institute of Health Stroke Scale; TOAST (Trial of Org 10172 in Acute Stroke Treatment); TICI (Thrombolysis in Cerebral Infarction); ICH (Intracranial Hemorrhage).
is of interest since their pathophysiological significance might be different. This distinction in the WMC grading is suitable for studies looking at the clinical significance of different radiological manifestations of cerebral small vessel disease. In this cohort, high WMC load on CT scan was related with clinical variables that are known to be associated with small vessel disease, such as older age and higher systolic blood pressure at admission, suggesting that the grading in CT scans does reflect brain lesions associated with chronic microvascular damage. An ideal WMC rating scale would be easy to perform both in CT and MRI scans. To our knowledge only 2 visual rating scales for WMC have been validated for both techniques [6,21]: the first one is the van Swieten scale, which is a three-point scale that grades WMC in anterior and posterior PV areas separately. This scale has a good reliability (weighted κ value 0.63 and 0.78 for CT and MRI respectively), but the agreement between the evaluation of this scale on CT and on MRI was not assessed [22]. The second validated score is the Age-Related White Matter Changes (ARWMC) designed by Wahlund et al., which grades 5 different cerebral territories but does not differentiate PV from DWM areas [23]. This scale showed a moderate interrater reliability for CT (κ = 0.48) and the sensitivity in extensive WMC lesions was similar on both CT and MRI, but it's main limitation is that it is complex for untrained physicians. Alternatively, the Fazekas scale allows a simple and global assessment of WMC, whereby it has been already employed in many clinical studies [4,18,24–26]. Although the accuracy of the scale on CT is inferior compared to MRI, especially for DWM areas, the Fazekas scale on CT is a reasonable alternative to brain MRI to grade WMC. This is relevant for patients with stroke whose early diagnosis and management is often based in brain CT. For example, severe WMC has been related to the risk of hemorrhagic complications after thrombolysis, [5] and the Fazekas score on CT could therefore be of interest in registries and studies analyzing the impact of white matter disease on the safety of revascularization or other acute therapies of stroke. Our study has several limitations. First, the external validity might be weakened by our selection criteria. Our cohort is younger and has less grade of small vessel disease compared to other cohorts of stroke patients, and the inclusion of patients with less WMC load may have underestimated the value of the Fazekas score on CT because the agreement between CT and MRI was better in patients with greater scores.
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Table 2 Associations between the dichotomized Fazekas score on CT scans and clinical data.
Age, (SD) Female sex n, (%) Hypertension, n (%) Diabetes mellitus, n (%) Dyslipidemia, n (%) Atrial fibrillation, n (%) SBP at admission, mmHg (SD) Glucose at admission, mg/dL (SD) NIHSS at admission, median (IQR) mRS 90 days, median (IQR) Good outcome, n (%) Symptomatic ICH, n (%) DWI volume, mean (SD)
Low PV load WMC N = 113 (72%)
High PV load WMC N = 44 (28%)
p
Low DWM load WMC N = 123 (78%)
High DWM load WMC N = 34 (22%)
p
62.8 (14.2) 50 (44.2) 61 (54) 18 (15.9) 41 (36.3) 35 (31) 143.4 (25.7) 131.0 (53.2) 15 (11−21) 2 (1–4) 82 (72.6) 6 (5.3) 45.5 (53.3)
74.1 (8.3) 26 (59.1) 31 (70) 10 (22.7) 20 (45.5) 21 (47.7) 153.4 (26.2) 134.3 (39.8) 16.5 (12–21) 3 (2–4) 26 (59.1) 2 (4.5) 33.2 (40.1)
b0.001 0.095 0.060 0.318 0.29 0.049 0.038 0.680 0.864 0.467 0.102 1 0.122
63.6 (14.2) 58 (47.2) 68 (55.3) 20 (16.3) 45 (36.6) 37 (30.1) 144.1 (25.9) 129.7 (52.0) 16.9 (12–21) 2 (1–4) 87 (70.7) 6 (4.9) 43.4 (51.7)
74.4 (7.5) 18 (52.9) 24 (70.6) 8 (23.5) 16 (47.1) 19 (55.9) 154 (26.2) 140.0 (41.0) 16 (11–21) 2.5 (2–4) 21 (61.8) 2 (5.9) 37.3 (44.2)
b0.001 0.550 0.109 0.327 0.267 0.005 0.059 0.227 0.920 0.788 0.318 0.684 0.505
SBP: systolic blood pressure; DWI: diffusion-weighted image; NIHSS: National Institute of Health Stroke Scale; mRS: modified Rankin scale; good outcome: mRS b 3 at 90 days.
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