التفاصيل البيبلوغرافية
| العنوان: |
Diagnostic and prognostic benefit of arterial spin labeling in subacute stroke. |
| المؤلفون: |
Thamm, Thoralf1,2 (AUTHOR), Zweynert, Sarah1,2 (AUTHOR) sarah.zweynert@charite.de, Piper, Sophie K.3,4 (AUTHOR), Madai, Vince I.1,5 (AUTHOR), Livne, Michelle1 (AUTHOR), Martin, Steve Z.1 (AUTHOR), Herzig, Cornelius X.1 (AUTHOR), Mutke, Matthias A.1,6 (AUTHOR), Siebert, Eberhard7 (AUTHOR), Liebig, Thomas7,8 (AUTHOR), Sobesky, Jan1,9 (AUTHOR) |
| المصدر: |
Brain & Behavior. May2019, Vol. 9 Issue 5, pN.PAG-N.PAG. 1p. |
| مصطلحات موضوعية: |
*SPIN labels, *STROKE, *MAGNETIC resonance imaging, *DIFFUSION magnetic resonance imaging, *DIAGNOSTIC imaging, *CEREBRAL arteries |
| مستخلص: |
Background and Purpose: Brain perfusion measurement in the subacute phase of stroke may support therapeutic decisions. We evaluated whether arterial spin labeling (ASL), a noninvasive perfusion imaging technique based on magnetic resonance imaging (MRI), adds diagnostic and prognostic benefit to diffusion‐weighted imaging (DWI) in subacute stroke. Methods: In a single‐center imaging study, patients with DWI lesion(s) in the middle cerebral artery (MCA) territory were included. Onset to imaging time was ≤7 days and imaging included ASL and DWI sequences. Qualitative (standardized visual analysis) and quantitative perfusion analyses (region of interest analysis) were performed. Dichotomized early outcome (modified Rankin Scale [mRS] 0–2 vs. 3–6) was analyzed in two logistic regression models. Model 1 included DWI lesion volume, age, vascular pathology, admission NIHSS, and acute stroke treatment as covariates. Model 2 added the ASL‐based perfusion pattern to Model 1. Receiver‐operating‐characteristic (ROC) and area‐under‐the‐curve (AUC) were calculated for both models to assess their predictive power. The likelihood‐ratio‐test compared both models. Results: Thirty‐eight patients were included (median age 70 years, admission NIHSS 4, onset to imaging time 67 hr, discharge mRS 2). Qualitative perfusion analysis yielded additional diagnostic information in 84% of the patients. In the quantitative analysis, AUC for outcome prediction was 0.88 (95% CI 0.77–0.99) for Model 1 and 0.97 (95% CI 0.91–1.00) for Model 2. Inclusion of perfusion data significantly improved performance and outcome prediction (p = 0.002) of stroke imaging. Conclusions: In patients with subacute stroke, our study showed that adding perfusion imaging to structural imaging and clinical data significantly improved outcome prediction. This highlights the usefulness of ASL and noninvasive perfusion biomarkers in stroke diagnosis and management. [ABSTRACT FROM AUTHOR] |
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