ArticleIBRO neuroscience reports2026
Prediction of early neurological deterioration using quantitative susceptibility mapping in small artery occlusion ischemic stroke.
Article in IBRO neuroscience reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
Background: Approximately 20-30% of ischemic stroke patients with small artery occlusion (SAO) experience early neurological deterioration (END) after stroke onset, which is associated with a poor prognosis. Our aim was to determine the predictive ability of quantitative susceptibility mapping (QSM) for END in patients with SAO. Methods: As neurological deficits, the National Institutes of Health Stroke Scale (NIHSS) was used. END was defined as an increase of ≥ 2 points on the NIHSS from baseline during the first week. 126 SAO stroke participants within 24 h of stroke onset were included in this study and divided into END group and nEND (no early neurological deterioration) group. QSM values were calculated from magnetic resonance imaging (MRI) data and clinical characteristics were collected. A cluster-based permutation test was conducted between groups and diagnostic efficacy of the QSM value was evaluated. Results: As a result, END group showed a different cortical susceptibility pattern compared with nEND group. Specifically, END group showed higher QSM value in the left/right precentral gyrus, the middle frontal gyrus, the opercular part of the left/right inferior frontal gyrus, the triangular part of left inferior frontal gyrus and postcentral gyrus, while lower in the dorsolateral part of left superior frontal. The support vector machine (SVM) classifier's analysis showed a high power for QSM diagnostic efficacy between END and nEND groups. In addition, homocysteine (Hcy), low-density lipoprotein (LDL), and total cholesterol (TC) were correlated with QSM values in some specific brain regions. Conclusion: The cluster-based permutation test indicated the differences of brain QSM value distribution between END and nEND groups. The SVM classifier's analysis showed a high power for QSM diagnostic efficacy between the two groups. Our study revealed the underlying predictive value of QSM in END of SAO stroke patients.
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