| 21. |
- Wu, Ona, et al.
(author)
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Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data
- 2019
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In: Stroke. - 1524-4628. ; 50:7, s. 1734-1741
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Journal article (peer-reviewed)abstract
- Background and Purpose- We evaluated deep learning algorithms' segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods- Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms' performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results- The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P<0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm3 (0.9-16.6 cm3). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P<0.0001) and different topography compared with other stroke subtypes. Conclusions- Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.
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| 22. |
- Bretzner, Martin, et al.
(author)
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Radiomics-Derived Brain Age Predicts Functional Outcome After Acute Ischemic Stroke.
- 2023
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In: Neurology. - 1526-632X .- 0028-3878. ; 100:8
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Journal article (peer-reviewed)abstract
- While chronological age is one of the most influential determinants of poststroke outcomes, little is known of the impact of neuroimaging-derived biological "brain age." We hypothesized that radiomics analyses of T2-FLAIR images texture would provide brain age estimates and that advanced brain age of patients with stroke will be associated with cardiovascular risk factors and worse functional outcomes.We extracted radiomics from T2-FLAIR images acquired during acute stroke clinical evaluation. Brain age was determined from brain parenchyma radiomics using an ElasticNet linear regression model. Subsequently, relative brain age (RBA), which expresses brain age in comparison with chronological age-matched peers, was estimated. Finally, we built a linear regression model of RBA using clinical cardiovascular characteristics as inputs and a logistic regression model of favorable functional outcomes taking RBA as input.We reviewed 4,163 patients from a large multisite ischemic stroke cohort (mean age = 62.8 years, 42.0% female patients). T2-FLAIR radiomics predicted chronological ages (mean absolute error = 6.9 years, r = 0.81). After adjustment for covariates, RBA was higher and therefore described older-appearing brains in patients with hypertension, diabetes mellitus, a history of smoking, and a history of a prior stroke. In multivariate analyses, age, RBA, NIHSS, and a history of prior stroke were all significantly associated with functional outcome (respective adjusted odds ratios: 0.58, 0.76, 0.48, 0.55; all p-values < 0.001). Moreover, the negative effect of RBA on outcome was especially pronounced in minor strokes.T2-FLAIR radiomics can be used to predict brain age and derive RBA. Older-appearing brains, characterized by a higher RBA, reflect cardiovascular risk factor accumulation and are linked to worse outcomes after stroke.
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| 25. |
- Morris, Andrew P, et al.
(author)
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Trans-ethnic kidney function association study reveals putative causal genes and effects on kidney-specific disease aetiologies
- 2019
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In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10:1
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Journal article (peer-reviewed)abstract
- Chronic kidney disease (CKD) affects ~10% of the global population, with considerable ethnic differences in prevalence and aetiology. We assemble genome-wide association studies of estimated glomerular filtration rate (eGFR), a measure of kidney function that defines CKD, in 312,468 individuals of diverse ancestry. We identify 127 distinct association signals with homogeneous effects on eGFR across ancestries and enrichment in genomic annotations including kidney-specific histone modifications. Fine-mapping reveals 40 high-confidence variants driving eGFR associations and highlights putative causal genes with cell-type specific expression in glomerulus, and in proximal and distal nephron. Mendelian randomisation supports causal effects of eGFR on overall and cause-specific CKD, kidney stone formation, diastolic blood pressure and hypertension. These results define novel molecular mechanisms and putative causal genes for eGFR, offering insight into clinical outcomes and routes to CKD treatment development.
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| 26. |
- Teo, Koon K., et al.
(author)
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Effects of telmisartan, irbesartan, valsartan, candesartan, and losartan on cancers in 15 trials enrolling 138 769 individuals The ARB Trialists Collaboration
- 2011
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In: Journal of Hypertension. - 0263-6352 .- 1473-5598. ; 29:4, s. 623-635
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Research review (peer-reviewed)abstract
- Background Angiotensin-converting enzyme inhibitors (ACEi) and angiotensin II receptor blockers (ARBs) reduce cardiovascular disease (CVD) events, but a recent meta-analysis of selected studies suggested that ARBs may increase cancer risks.Objective Candesartan, irbesartan, telmisartan, valsartan, and losartan were assessed for incident cancers in 15 large parallel long-term multicenter double-blind clinical trials of these agents involving 138 769 participants.Patients and methods Individuals at high CVD risk were randomized to telmisartan (three trials, n=51 878), irbesartan (three trials, n=14 859), valsartan (four trials, n=44 264), candesartan (four trials, n=18 566), and losartan (one trial, n=9193) and followed for 23-60 months. Incident cancer cases were compared in patients randomized to ARBs versus controls. In five trials (n=42 403), the ARBs were compared to ACEi and in 11 trials (n=63 313) to controls without ACEi. In addition, in seven trials (n=47 020), the effect of ARBs with ACEi was compared to ACEi alone and in two trials ARBs with ACEi versus ARB alone (n=25 712).Results Overall, there was no excess of cancer incidence with ARB therapy compared to controls in the 15 trials [ 4549 (6.16%) cases of 73 808 allocated to ARB versus 3856 (6.31%) of 61 106 assigned to non-ARB controls; odds ratio (OR) 1.00, 95% confidence interval (CI) 0.95-1.04] overall or when individual ARBs were examined. ORs comparing combination therapy with ARB along with ACEi versus ACEi was 1.01 (95% CI 0.94-1.10), combination versus ARB alone 1.02 (95% CI 0.91-1.13), ARB alone versus ACEi alone 1.06 (95% CI 0.97-1.16) and ARB versus placebo/control without ACEi 0.97 (95% CI 0.91-1.04). There was no excess of lung, prostate or breast cancer, or overall cancer deaths associated with ARB treatment.Conclusion There was no significant increase in the overall or site-specific cancer risk from ARBs compared to controls.
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