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Cited 56 time in webofscience Cited 58 time in scopus
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Grading and Interpretation of White Matter Hyperintensities Using Statistical Mapsopen access

Authors
Ryu, Wi-SunWoo, Sung-HoSchellingerhout, DawidChung, Moo K.Kim, Chi KyungJang, Min UkPark, Kyoung-JongHong, Keun-SikJeong, Sang-WukNa, Jeong-YongCho, Ki-HyunKim, Joon-TaeKim, Beom JoonHan, Moon-KuLee, JunCha, Jae-KwanKim, Dae-HyunLee, Soo JooKo, YoungchaiCho, Yong-JinLee, Byung-ChulYu, Kyung-HoOh, Mi-SunPark, Jong-MooKang, KyusikLee, Kyung BokPark, Tai HwanLee, JuneyoungChoi, Heung-KookLee, KiwonBae, Hee-JoonKim, Dong-Eog
Issue Date
Dec-2014
Publisher
LIPPINCOTT WILLIAMS & WILKINS
Keywords
cerebral infarction; leukoaraiosis; magnetic resonance imaging; topographic brain mapping
Citation
STROKE, v.45, no.12, pp 3567 - 3575
Pages
9
Indexed
SCI
SCIE
SCOPUS
Journal Title
STROKE
Volume
45
Number
12
Start Page
3567
End Page
3575
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/18337
DOI
10.1161/STROKEAHA.114.006662
ISSN
0039-2499
1524-4628
Abstract
Background and Purpose-We aimed to generate rigorous graphical and statistical reference data based on volumetric measurements for assessing the relative severity of white matter hyperintensities (WMHs) in patients with stroke. Methods-We prospectively mapped WMHs from 2699 patients with first-ever ischemic stroke (mean age=66.8 +/- 13.0 years) enrolled consecutively from 11 nationwide stroke centers, from patient (fluid-attenuated-inversion-recovery) MRIs onto a standard brain template set. Using multivariable analyses, we assessed the impact of major (age/hypertension) and minor risk factors on WMH variability. Results-We have produced a large reference data library showing the location and quantity of WMHs as topographical frequency-volume maps. This easy-to-use graphical reference data set allows the quantitative estimation of the severity of WMH as a percentile rank score. For all patients (median age=69 years), multivariable analysis showed that age, hypertension, atrial fibrillation, and left ventricular hypertrophy were independently associated with increasing WMH (0-9.4%, median=0.6%, of the measured brain volume). For younger (<= 69) hypertensives (n=819), age and left ventricular hypertrophy were positively associated with WMH. For older (>= 70) hypertensives (n=944), age and cholesterol had positive relationships with WMH, whereas diabetes mellitus, hyperlipidemia, and atrial fibrillation had negative relationships with WMH. For younger nonhypertensives (n=578), age and diabetes mellitus were positively related to WMH. For older nonhypertensives (n=328), only age was positively associated with WMH. Conclusions-We have generated a novel graphical WMH grading (Kim statistical WMH scoring) system, correlated to risk factors and adjusted for age/hypertension. Further studies are required to confirm whether the combined data set allows grading of WMH burden in individual patients and a tailored patient-specific interpretation in ischemic stroke-related clinical practice.
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