White matter hyperintensity burden in acute stroke patients differs by ischemic stroke subtype

Schirmer, Markus D.; Dalca, Adrian V.; Sridharan, Ramesh; Donahue, Kathleen; Nardin, Marco; Irie, Robert; McIntosh, Elissa C.; Mocking, Steven J. T.; Xu, Huichun; Cole, John W.; Giralt‐Steinhauer, Eva; Jiménez‐Conde, Jordi; Jern, Christina; Kleindorfer, Dawn; Lemmens, Robin; Wassélius, Johan; Lindgren, Arne; Rundek, Tatjana; Sacco, Ralph L.; Schmidt, Reinhold; Sharma, Pankaj; Słowik, Agnieszka; Thijs, Vincent; Worrall, Bradford B.; Woo, Daniel; Kittner, Steven J.; McArdle, Patrick F.; Mitchell, Braxton D.; Rosand, Jonathan; Meschia, James F.; Wu, Ona; Golland, Polina; Rost, Natalia S.

doi:10.1212/wnl.0000000000009728

Cited by 41 publications

(44 citation statements)

References 33 publications

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“…Figure 5 presents a list of the top 10 authors. Dr. Ona Wu [11,66,67] from Harvard Medical School, Boston, United States, has published more documents (7) related to the research area of this review, and this correlates with his publication record as documented in the Scopus database related to ischemic stroke. Figure 5.…”

Section: Magnetic Resonancementioning

confidence: 79%

“…Classification of the top 10 authors according to the first criterion of search. In the figure, it can be seen that Dr. Ona Wu [11,66,67] from Harvard Medical School, Boston, United States, has published more documents (7) related to the research area of this review. To analyze and answer the three central research questions of this work, the global search of the 140 documents was further refined.…”

Section: Magnetic Resonancementioning

confidence: 99%

“…Even though there are a large number of publications related to ischemic stroke (27 documents) and most deal with the classification of stroke patients versus normal controls, or the prediction of post-stroke functional impairment or treatment outcome [21,25,26,28,33,38,42,66,67,72,74,75,77,78,80,167,168,179,[184][185][186][187][188][189][190][193][194][195], there is a paucity of results related to demyelinating diseases alone. However, there are some publications dealing with multiple sclerosis (MS), which is the most common demyelinating disease (2) [191,192].…”

Section: And DL Classifiers Applied To Diagnosis Of Ischemia and Dmentioning

confidence: 99%

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MR Images, Brain Lesions, and Deep Learning

2021

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Medical brain image analysis is a necessary step in computer-assisted/computer-aided diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of white matter hyperintensities (WMHs) of brain magnetic resonance (MR) images, specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used bibliometric networks. Of a total of 140 documents, we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and development of new deep learning models to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with good performance metrics (e.g., Dice similarity coefficient, DSC: 0.99) were found; however, there is little practical application due to the use of small datasets and a lack of reproducibility. Therefore, the main conclusion is that there should be multidisciplinary research groups to overcome the gap between CAD developments and their deployment in the clinical environment.

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Section: Magnetic Resonancementioning

confidence: 79%

Section: Magnetic Resonancementioning

confidence: 99%

Section: And DL Classifiers Applied To Diagnosis Of Ischemia and Dmentioning

confidence: 99%

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MR Images, Brain Lesions, and Deep Learning

2021

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“…Cerebral SVDs are proposed as a surrogate or biomarker of stroke and dementia. The hallmark of cerebral SVD is mainly endothelial changes in the cerebral arterioles and microvessels that present different features on magnetic resonance imaging (MRI), such as white matter hyperintensities (WMHs), cerebral microbleeds (CMBs), covert infarcts (CIs), and large perivascular space [ 42 , 43 ]. Large perivascular spaces were associated with rapid deterioration of information processing speed and a four-fold risk of developing VaD [ 44 ].…”

Section: Resultsmentioning

confidence: 99%

Genetic Variants behind Cardiovascular Diseases and Dementia

Chen

2020

Genes

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Cardiovascular diseases (CVDs) and dementia are the leading causes of disability and mortality. Genetic connections between cardiovascular risk factors and dementia have not been elucidated. We conducted a scoping review and pathway analysis to reveal the genetic associations underlying both CVDs and dementia. In the PubMed database, literature was searched using keywords associated with diabetes mellitus, hypertension, dyslipidemia, white matter hyperintensities, cerebral microbleeds, and covert infarctions. Gene lists were extracted from these publications to identify shared genes and pathways for each group. This included high penetrance genes and single nucleotide polymorphisms (SNPs) identified through genome wide association studies. Most risk SNPs to both diabetes and dementia participate in the phospholipase C enzyme system and the downstream nositol 1,4,5-trisphosphate and diacylglycerol activities. Interestingly, AP-2 (TFAP2) transcription factor family and metabolism of vitamins and cofactors were associated with genetic variants that were shared by white matter hyperintensities and dementia, and by microbleeds and dementia. Variants shared by covert infarctions and dementia were related to VEGF ligand–receptor interactions and anti-inflammatory cytokine pathways. Our review sheds light on future investigations into the causative relationships behind CVDs and dementia, and can be a paradigm of the identification of dementia treatments.

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“…Age was the clinical aspect correlating most strongly with the radiomic signature of WMH burden and is a well-established predictor of WMH. 10,28 Similarly, blood-brain barrier studies using PWI highlight an age-associated increased leakage of contrast agents within WMH, but also beyond, in NAWM, showcasing a possible preclinical pathogenic step leading to cognitive decline. 29 Our findings also suggest the presence of age-related subvisible abnormalities that can notably be quantified on structural T2-FLAIR images.…”

Section: Discussionmentioning

confidence: 99%

MRI Radiomic Signature of White Matter Hyperintensities Is Associated with Clinical Phenotypes

Bretzner

Bonkhoff

Schirmer

et al. 2021

Preprint

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IntroductionNeuroimaging measurements of brain structural integrity are thought to be surrogates for brain health, but precise assessments require dedicated advanced image acquisitions. By means of describing the texture of conventional images beyond what meets the naked eye, radiomic analyses hold potential for evaluating brain health. We sought to: 1) evaluate this novel approach to assess brain structural integrity by predicting white matter hyperintensities burdens (WMH) and 2) uncover associations between predictive radiomic features and patients’ clinical phenotypes.MethodsOur analyses were based on a multi-site cohort of 4,163 acute ischemic strokes (AIS) patients with T2-FLAIR MR images and corresponding deep-learning-generated total brain and WMH segmentation. Radiomic features were extracted from normal-appearing brain tissue (brain mask–WMH mask). Radiomics-based prediction of personalized WMH burden was done using ElasticNet linear regression. We built a radiomic signature of WMH with the most stable selected features predictive of WMH burden and then related this signature to clinical variables (age, sex, hypertension (HTN), atrial fibrillation (AF), diabetes mellitus (DM), coronary artery disease (CAD), and history of smoking) using canonical correlation analysis.ResultsRadiomic features were highly predictive of WMH burden (R2=0.855±0.011). Seven pairs of canonical variates (CV) significantly correlated the radiomics signature of WMH and clinical traits with respective canonical correlations of 0.81, 0.65, 0.42, 0.24, 0.20, 0.15, and 0.15 (FDR-corrected p-valuesCV1-6<.001, p-valueCV7=.012). The clinical CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and DM, CV4 by HTN, CV5 by AF and DM, CV6 by CAD, and CV7 by CAD and DM.ConclusionRadiomics extracted from T2-FLAIR images of AIS patients capture microstructural damage of the cerebral parenchyma and correlate with clinical phenotypes. Further research could evaluate radiomics to predict the progression of WMH.Research in contextEvidence before this studyWe did a systematic review on PubMed until December 1, 2020, for original articles and reviews in which radiomics were used to characterize stroke or cerebrovascular diseases. Radiomic analyses cover a broad ensemble of high-throughput quantification methods applicable to digitalized medical images that extract high-dimensional data by describing a given region of interest by its size, shape, histogram, and relationship between voxels. We used the search terms “radiomics” or “texture analysis”, and “stroke”, “cerebrovascular disease”, “small vessel disease”, or “white matter hyperintensities”. Our research identified 24 studies, 18 studying radiomics of stroke lesions and 6 studying cerebrovascular diseases. All the latter six studies were based on MRI (T1-FLAIR, dynamic contrast-enhanced imaging, T1 & T2-FLAIR, T2-FLAIR post-contrast, T2-FLAIR, and T2-TSE images). Four studies were describing small vessel disease, and two were predicting longitudinal progression of WMH. The average sample size was small with 96 patients included (maximum: 204). One study on 141 patients identified 7 T1-FLAIR radiomic features correlated with cardiovascular risk factors (age and hyperlipidemia) using univariate correlations. All studies were monocentric and performed on a single MRI scanner.Added value of this studyTo date and to the best of our knowledge, this is the largest radiomics study performed on cerebrovascular disease or any topic, and one of the very few to include a great diversity of participating sites with diverse clinical MRI scanners. This study is the first one to establish a radiomic signature of WMH and to interpret its relationship with common cardiovascular risk factors. Our findings add to the body of evidence that damage caused by small vessel disease extend beyond the visible white matter hyperintensities, but the added value resides in the detection of that subvisible damage on routinely acquired T2-FLAIR imaging. It also suggests that cardiovascular phenotypes might manifest in distinct textural patterns detectable on conventional clinical-grade T2-FLAIR images.Implications of all the available evidenceAssessing brain structural integrity has implications for treatment selection, follow-up, prognosis, and recovery prediction in stroke patients but also other neurological disease populations. Measuring cerebral parenchymal structural integrity usually requires advanced imaging such as diffusion tensor imaging or functional MRI. Translation of those neuroimaging biomarkers remains uncommon in clinical practice mainly because of their time-consuming and costly acquisition. Our study provides a potential novel solution to assess brains’ structural integrity applicable to standard, routinely acquired T2-FLAIR imaging.Future research could, for instance, benchmark this radiomics approach against diffusion or functional MRI metrics in the prediction of cognitive or functional outcomes after stroke.

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White matter hyperintensity burden in acute stroke patients differs by ischemic stroke subtype

Cited by 41 publications

References 33 publications

MR Images, Brain Lesions, and Deep Learning

MR Images, Brain Lesions, and Deep Learning

Genetic Variants behind Cardiovascular Diseases and Dementia

MRI Radiomic Signature of White Matter Hyperintensities Is Associated with Clinical Phenotypes

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