Prevalence and Significance of the Vessel-Cluster Sign on Susceptibility-Weighted Imaging in Patients With Severe Small Vessel Disease

Rudilosso, Salvatore; Chui, E.; Stringer, Michael; Thrippleton, Michael J.; Chappell, Francesca M; Blair, Gordon W.; GarcÃa, Daniela Jaime; Doubal, Fergus; Hamilton, I.; Kopczak, Anna; Ingrisch, Michael; Kerkhofs, Danielle; Backes, Walter H.; Staals, Julie; Oostenbrugge, Robert J. van; Duering, Marco; Dichgans, Martin

doi:10.1212/wnl.0000000000200614

Cited by 16 publications

(6 citation statements)

References 34 publications

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“…This result is consistent with the results of a recent study in the same cohort of patients showing altered CVR in the white matter surrounding cavities that show clusters thought to be dilated small vessels on SWI, which seems to presage tissue destruction and lacune formation. 22 Male sex was associated with hotspots corresponding to lacunes but not with WMH in multivariable models in both logistic and ordinal regressions. In this cohort, there was a strong association of male sex with the presence of hotspots corresponding to any SVD markers in the univariable analysis, which could be in part due to the higher number of lacunes in males compared to females.…”

Section: Discussionmentioning

confidence: 91%

Blood-brain barrier leakage hotspots collocating with brain lesions due to sporadic and monogenic small vessel disease

Rudilosso

Stringer

Thrippleton

et al. 2023

J Cereb Blood Flow Metab

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Blood-brain barrier (BBB) is known to be impaired in cerebral small vessel disease (SVD), and is measurable by dynamic-contrast enhancement (DCE)-MRI. In a cohort of 69 patients (42 sporadic, 27 monogenic SVD), who underwent 3T MRI, including DCE and cerebrovascular reactivity (CVR) sequences, we assessed the relationship of BBB-leakage hotspots to SVD lesions (lacunes, white matter hyperintensities (WMH), and microbleeds). We defined as hotspots the regions with permeability surface area product highest decile on DCE-derived maps within the white matter. We assessed factors associated with the presence and number of hotspots corresponding to SVD lesions in multivariable regression models adjusted for age, WMH volume, number of lacunes, and SVD type. We identified hotspots at lacune edges in 29/46 (63%) patients with lacunes, within WMH in 26/60 (43%) and at the WMH edges in 34/60 (57%) patients with WMH, and microbleed edges in 4/11 (36%) patients with microbleeds. In adjusted analysis, lower WMH-CVR was associated with presence and number of hotspots at lacune edges, and higher WMH volume with hotspots within WMH and at WMH edges, independently of the SVD type. In conclusion, SVD lesions frequently collocate with high BBB-leakage in patients with sporadic and monogenic forms of SVD.

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Section: Discussionmentioning

confidence: 91%

Blood-brain barrier leakage hotspots collocating with brain lesions due to sporadic and monogenic small vessel disease

Rudilosso

Stringer

Thrippleton

et al. 2023

J Cereb Blood Flow Metab

Self Cite

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“…On one side, it was a cross-sectional survey and did not follow up on the patients’ sense of stigma. On the other hand, because small vessel disease of the brain is a heterogeneous series of pathophysiological processes [ 43 , 44 ], with assorted mechanisms and clinical outcomes, it is necessary to narrow the type of stroke population [ 45 ], Therefore, we recommend that future studies investigate and analyze the relationship and relevance of small vessel disease versus other ischemic subtypes at the sigma level.…”

Section: Discussionmentioning

confidence: 99%

An investigation of the level of stigma and the factors influencing it in the rehabilitation of young and middle-aged stroke patients-a cross-sectional study

et al. 2023

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Background There are few reported studies on stigma in young and middle-aged stroke patients during the rehabilitation period, however, the rehabilitation period plays a key role in the patients’ disease regression. Exploring the level of stigma and the influencing factors in young and middle-aged stroke patients during the rehabilitation period is crucial for determining how to reduce the level of stigma and improve the patients’ motivation for rehabilitation treatment. Therefore, this study investigated the level of stigma in young and middle-aged stroke patients and analyzed the factors influencing stigma in order to provide a reference or basis for healthcare professionals to develop effective and targeted stigma intervention programs. Methods Using a convenience sampling method, 285 young and middle-aged stroke patients admitted to the rehabilitation medicine department of a tertiary care hospital in Shenzhen, China, from November 2021 to September 2022 were selected and surveyed using a general information questionnaire, the Stroke Stigma Scale(SSS), the Barthel Index(BI), and the Positive and Negative Emotions Scale(PANAS), and multiple linear regression and smoothed curve fitting were used to analyze the factors influencing the stigma of young and middle-aged stroke patients during the rehabilitation period. Results SSS score of 45.08 ± 11.06, univariate analysis of age, occupation, education level, pre-stroke monthly income, insurance type, comorbid chronic disease status, primary caregiver, BI, positive and negative emotion as factors influencing stigma. Multiple linear regression showed that age, pre-stroke monthly income, BI, positive and negative emotions were independent influences on stigma in young and middle-aged stroke patients, explaining 58.0% of the total variance in stigma. A smoothed curve fit revealed a curvilinear relationship between the above influences and stigma. Conclusion Young and middle-aged stroke patients have a moderate level of stigma. Medical staff should focus on young patients aged 18–44 years, those with high monthly income before the stroke, those with poor self-care ability, and those with low positive and high negative emotion scores, and conduct early assessments and adopt targeted intervention programs according to the influencing factors to reduce the stigma of young and middle-aged stroke patients, improve their motivation for rehabilitation, and help them return to their families and society as soon as possible. Trial Registration Registration number of China Clinical Trials Registration Center: 20,220,328,004-FS01.

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“…13,14 CMBs also serve as neuroimaging biomarkers for CSVD and play a critical role in the diagnosis of CSVD, especially CAA. 13,[15][16][17] Hence, accurate identification of CMBs is useful for evaluating the risk of intracerebral hemorrhage; facilitating classification of CSVD based on the presence of CMBs, both of which are beneficial for optimizing CSVD management.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning using Susceptibility-Weighted MR Sequence to Detect Microbleeds and Classify Cerebral Small Vessel Disease

Liu

Li³

et al. 2023

Preprint

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Abstract<break><break>Background: Microbleeds (CMBs) serve as neuroimaging biomarkers to assess risk of intracerebral hemorrhage and diagnose cerebral small vessel disease (CSVD). Therefore, detectingCMBs can evaluate the risk of intracerebral hemorrhage and use its presence to support CSVDclassification, both are conducive to optimizing CSVD management. This study aimed to develop and test a deep learning (DL) model based on susceptibility-weighted MR sequence (SWS) to detect CMBs and classify CSVD to assist neurologists in optimizing CSVD management.<break><break>Methods: Patients with arteriolosclerosis (aSVD), cerebral amyloid angiopathy (CAA), and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) treated at three centers were enrolled between January 2017 and May 2022 in this retrospective study.The SWSs of patients from two centers were used as the development set, and the SWSs of patientsfrom the remaining center were used as the external test set. The DL model contains a Mask R-CNN for detecting CMBs and a multi-instance learning (MIL) network for classifying CSVD. The metrics for model performance included intersection over union (IoU), Dice score, recall, confusion matrices, receiver operating characteristic curve (ROC) analysis, accuracy, precision, and F1-score.<break><break>Results: A total of 364 SWS were recruited, including 336 in the development set and 28 in the external test set. IoU for the model was 0.523 0.319, Dice score 0.627 0.296, and recall 0.706 0.365 for CMBs detection in the external test set. For CSVD classification, the model achieved a weighted-average AUC of 0.908 (95% CI: 0.895-0.921), accuracy of 0.819 (95% CI: 0.768-0.870),weighted-average precision of 0.864 (95% CI: 0.831-0.897), and weighted-average F1-score of 0.829 (95% CI: 0.782-0.876) in the external set, outperforming the performance of the neurologist group.<break><break>Conclusion: The DL model based on SWS can detect CMBs and classify CSVD, thereby assisting neurologists in optimizing CSVD management.

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Prevalence and Significance of the Vessel-Cluster Sign on Susceptibility-Weighted Imaging in Patients With Severe Small Vessel Disease

Cited by 16 publications

References 34 publications

Blood-brain barrier leakage hotspots collocating with brain lesions due to sporadic and monogenic small vessel disease

Blood-brain barrier leakage hotspots collocating with brain lesions due to sporadic and monogenic small vessel disease

An investigation of the level of stigma and the factors influencing it in the rehabilitation of young and middle-aged stroke patients-a cross-sectional study

Deep Learning using Susceptibility-Weighted MR Sequence to Detect Microbleeds and Classify Cerebral Small Vessel Disease

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