A systematic review of the usefulness of magnetic resonance imaging in predicting the gait ability of stroke patients.
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ABSTRACT:
SUBMITTER: Imura T
PROVIDER: S-EPMC8275756 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background and purposePatients with transient ischemic attacks (TIA) have a significant risk of developing acute ischemic strokes (AIS), emphasizing the critical need for hierarchical management. This study aims to develop a clinical-imaging model utilizing multimodal magnetic resonance imaging (mMRI) and the revised Framingham Stroke Risk Profile (FSRP) to predict AIS and achieve early secondary prevention.MethodsmMRI scans were conducted on patients with symptomatic intracranial atherosclerotic disease (ICAD) to assess vascular wall features and cerebral perfusion parameters. Based on diffusion-weighted imaging (DWI), patients were divided into two groups: TIA and AIS. Clinical data were evaluated to calculate the FSRP score. Differences in clinical and imaging characteristics between the groups were analyzed, and a predictive model for AIS probability in patients with ICAD was established.ResultsA total of 112 TIA and AIS patients were included in the study. The results showed that the AIS group had higher proportions of FSRP-high risk, hyperhomocysteinemia, and higher value of low-density lipoprotein (LDL), standardized plaque index (SQI), and enhancement rate (ER) compared to the TIA group (p < 0.05). Mean transit time (MTT) and time to peak (TTP) in the lesion area were significantly longer in the AIS group (p < 0.05). Multivariate analysis identified FSRP-high risk (p = 0.027) and high ER (p = 0.046) as independent risk factors for AIS. The combined clinical and mMRI model produced an area under the curve (AUC) of 0.791 in receiver operating characteristic (ROC) analysis. The constructed nomogram model combining clinical and mMRI features demonstrated favorable clinical net benefits.ConclusionFSRP-high risk and high ER were confirmed as independent risk factors for AIS. The combined prediction model utilizing clinical and imaging markers effectively predicts stroke risk in symptomatic ICAD patients.
| S-EPMC10568328 | biostudies-literature
Project description:(1) Background: Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks to improve generalization in the tasks of interest. In magnetic resonance imaging (MRI), transfer learning is important for developing strategies that address the variation in MR images from different imaging protocols or scanners. Additionally, transfer learning is beneficial for reutilizing machine learning models that were trained to solve different (but related) tasks to the task of interest. The aim of this review is to identify research directions, gaps in knowledge, applications, and widely used strategies among the transfer learning approaches applied in MR brain imaging; (2) Methods: We performed a systematic literature search for articles that applied transfer learning to MR brain imaging tasks. We screened 433 studies for their relevance, and we categorized and extracted relevant information, including task type, application, availability of labels, and machine learning methods. Furthermore, we closely examined brain MRI-specific transfer learning approaches and other methods that tackled issues relevant to medical imaging, including privacy, unseen target domains, and unlabeled data; (3) Results: We found 129 articles that applied transfer learning to MR brain imaging tasks. The most frequent applications were dementia-related classification tasks and brain tumor segmentation. The majority of articles utilized transfer learning techniques based on convolutional neural networks (CNNs). Only a few approaches utilized clearly brain MRI-specific methodology, and considered privacy issues, unseen target domains, or unlabeled data. We proposed a new categorization to group specific, widely-used approaches such as pretraining and fine-tuning CNNs; (4) Discussion: There is increasing interest in transfer learning for brain MRI. Well-known public datasets have clearly contributed to the popularity of Alzheimer's diagnostics/prognostics and tumor segmentation as applications. Likewise, the availability of pretrained CNNs has promoted their utilization. Finally, the majority of the surveyed studies did not examine in detail the interpretation of their strategies after applying transfer learning, and did not compare their approach with other transfer learning approaches.
| S-EPMC8321322 | biostudies-literature
Project description:Cerebrovascular reactivity (CVR) magnetic resonance imaging (MRI) probes cerebral haemodynamic changes in response to a vasodilatory stimulus. CVR closely relates to the health of the vasculature and is therefore a key parameter for studying cerebrovascular diseases such as stroke, small vessel disease and dementias. MRI allows in vivo measurement of CVR but several different methods have been presented in the literature, differing in pulse sequence, hardware requirements, stimulus and image processing technique. We systematically reviewed publications measuring CVR using MRI up to June 2020, identifying 235 relevant papers. We summarised the acquisition methods, experimental parameters, hardware and CVR quantification approaches used, clinical populations investigated, and corresponding summary CVR measures. CVR was investigated in many pathologies such as steno-occlusive diseases, dementia and small vessel disease and is generally lower in patients than in healthy controls. Blood oxygen level dependent (BOLD) acquisitions with fixed inspired CO2 gas or end-tidal CO2 forcing stimulus are the most commonly used methods. General linear modelling of the MRI signal with end-tidal CO2 as the regressor is the most frequently used method to compute CVR. Our survey of CVR measurement approaches and applications will help researchers to identify good practice and provide objective information to inform the development of future consensus recommendations.
| S-EPMC7947694 | biostudies-literature
Project description:The Stroke Therapy Academic Industry Roundtable (STAIR) has recommended that novel therapeutics be tested in a large animal model with similar anatomy and physiology to humans. The pig is an attractive model due to similarities in brain size, organization, and composition relative to humans. However, multiple pig breeds have been used to study ischemic stroke with potentially differing cerebral anatomy, architecture and, consequently, ischemic stroke pathologies. The objective of this study was to characterize brain anatomy and assess spatiotemporal gait parameters in Yucatan (YC) and Landrace (LR) pigs pre- and post-stroke using magnetic resonance imaging (MRI) and gait analysis, respectively. Ischemic stroke was induced via permanent middle cerebral artery occlusion (MCAO). MRI was performed pre-stroke and 1-day post-stroke. Structural and diffusion-tensor sequences were performed at both timepoints and analyzed for cerebral characteristics, lesion diffusivity, and white matter changes. Spatiotemporal and relative pressure gait measurements were collected pre- and 2-days post-stroke to characterize and compare acute functional deficits. The results from this study demonstrated that YC and LR pigs exhibit differences in gross brain anatomy and gait patterns pre-stroke with MRI and gait analysis showing statistical differences in the majority of parameters. However, stroke pathologies in YC and LR pigs were highly comparable post-stroke for most evaluated MRI parameters, including lesion volume and diffusivity, hemisphere swelling, ventricle compression, caudal transtentorial and foramen magnum herniation, showing no statistical difference between the breeds. In addition, post-stroke changes in velocity, cycle time, swing percent, cadence, and mean hoof pressure showed no statistical difference between the breeds. These results indicate significant differences between pig breeds in brain size, anatomy, and motor function pre-stroke, yet both demonstrate comparable brain pathophysiology and motor outcomes post-stroke. The conclusions of this study suggest pigs of these different breeds generally show a similar ischemic stroke response and findings can be compared across porcine stroke studies that use different breeds.
| S-EPMC7859633 | biostudies-literature
Project description:Introduction: Vessel-wall magnetic resonance imaging (MRI) has been suggested as a valuable tool for assessing intracranial arterial stenosis with additional diagnostic features. However, there is limited conclusive evidence on whether vessel-wall MR imaging of intracranial atherosclerotic plaques provides valuable information for predicting vulnerable lesions. We conducted this systematic review and meta-analysis to evaluate which characteristics of intracranial-plaque on vessel-wall MRI are markers of culprit lesions. Methods: The MEDLINE, EMBASE, and Cochrane Library of Clinical Trials databases were searched for studies reporting the association between vessel-wall MRI characteristics of intracranial plaque and corresponding stroke events. Odds ratios (ORs) for the prevalence of stroke with intracranial-plaque MRI characteristics were pooled in a meta-analysis using a random-effects model. Results: Twenty studies were included in this review. We found a significant association between plaque enhancement (OR, 10.09; 95% CI, 5.38-18.93), positive remodeling (OR, 6.19; 95% CI, 3.22-11.92), and plaque surface irregularity (OR, 3.94; 95% CI, 1.90-8.16) with stroke events. However, no significant difference was found for the presence of eccentricity (OR, 1.22; 95% CI, 0.51-2.91). Conclusion: Based on current evidence, intracranial plaque contrast enhancement, positive remodeling, and plaque irregularity on MRI are associated with increased risk of stroke events. Our findings support the design of future studies on intracranial-plaque MRI and decision making for the management of intracranial atherosclerotic plaques.
| S-EPMC6287366 | biostudies-literature
Project description:MRI-based biomechanical studies can provide a deep understanding of the mechanisms governing liver function, its mechanical performance but also liver diseases. In addition, comprehensive modeling of the liver can help improve liver disease treatment. Furthermore, such studies demonstrate the beginning of an engineering-level approach to how the liver disease affects material properties and liver function. Aimed at researchers in the field of MRI-based liver simulation, research articles pertinent to MRI-based liver modeling were identified, reviewed, and summarized systematically. Various MRI applications for liver biomechanics are highlighted, and the limitations of different viscoelastic models used in magnetic resonance elastography are addressed. The clinical application of the simulations and the diseases studied are also discussed. Based on the developed questionnaire, the papers' quality was assessed, and of the 46 reviewed papers, 32 papers were determined to be of high-quality. Due to the lack of the suitable material models for different liver diseases studied by magnetic resonance elastography, researchers may consider the effect of liver diseases on constitutive models. In the future, research groups may incorporate various aspects of machine learning (ML) into constitutive models and MRI data extraction to further refine the study methodology. Moreover, researchers should strive for further reproducibility and rigorous model validation and verification.
| S-EPMC8493836 | biostudies-literature
Project description:The indispensability of visual working memory (VWM) in human daily life suggests its importance in higher cognitive functions and neurological diseases. However, despite the extensive research efforts, most findings on the neural basis of VWM are limited to a unimodal context (either structure or function) and have low generalization. To address the above issues, this study proposed the usage of multimodal neuroimaging in combination with machine learning to reveal the neural mechanism of VWM across a large cohort (N = 547). Specifically, multimodal magnetic resonance imaging features extracted from voxel-wise amplitude of low-frequency fluctuations, gray matter volume, and fractional anisotropy were used to build an individual VWM capacity prediction model through a machine learning pipeline, including the steps of feature selection, relevance vector regression, cross-validation, and model fusion. The resulting model exhibited promising predictive performance on VWM (r = .402, p < .001), and identified features within the subcortical-cerebellum network, default mode network, motor network, corpus callosum, anterior corona radiata, and external capsule as significant predictors. The main results were then compared with those obtained on emotional regulation and fluid intelligence using the same pipeline, confirming the specificity of our findings. Moreover, the main results maintained well under different cross-validation regimes and preprocess strategies. These findings, while providing richer evidence for the importance of multimodality in understanding cognitive functions, offer a solid and general foundation for comprehensively understanding the VWM process from the top down.
| S-EPMC7927291 | biostudies-literature
Project description:Accumulating studies had been performed using magnetic resonance imaging (MRI) to understand the neural mechanism of acupuncture therapy for depression. However, inconsistencies remain due to differences in research designs and MRI analytical methods. Therefore, we aim to summarize the current MRI research and provide useful information for further research by identifying papers published in English and Chinese about MRI studies on acupuncture for depression up to November 2020. A total of 22 studies met the inclusion criteria, including 810 depression patients and 416 health controls (HCs). The applied designs of these studies are mainly random control trial and pre-post designs. The MRI analytical methods are mainly (fractional) amplitude of low-frequency fluctuation (fALFF/ALFF) and functional connectivity (FC), whereas a small subset of studies used voxel-based morphometry (VBM) and diffusion tensor imaging (DTI). The most consistent functional MRI (fMRI) results showed increased N-acetylaspartate/creatine (NAA/Cr) ratios, increased ALFF in the right precuneus, decreased ALFF in the inferior frontal gyrus (IFG), and increased FC of the anterior cingulate cortex (ACC). In contrast, no significant neurological changes were identified in any of the DTI or VBM studies. However, clear, reliable conclusions cannot be drawn due to the use of different designs, analytical methods, seed points selected, types of depression, acupuncture points, and so on. Improved report specifications, well-designed studies, consistent analytical methods, and larger sample sizes will enable the field to better elucidate the underlying mechanisms of acupuncture in depressed patients.
| S-EPMC8417590 | biostudies-literature
Project description:Genome wide DNA methylation profiling of normal and tumour prostate samples. The Illumina Infinium MethylationEPIC Human DNA methylation oligonucleotide beads was used to obtain DNA methylation profiles across approximately 850,000 CpGs. Comparative assessment was carried out.
2018-12-01 | GSE101908 | GEO
Project description:IntroductionRobot-assisted devices have been recently developed for use in prostate biopsy. However, it is possible advantages over standard biopsy remain unclear. We aimed to assess the diagnostic performance and safety of robot-assisted targeted (RA-TB) and systematic prostate biopsies (RA-SB).MethodsA systematic literature search was performed in MEDLINE and Scopus databases. The detailed search strategy is available at Prospero (CRD42021269290). The primary outcome was the clinically significant prostate cancer (PCa) detection rate. The secondary outcomes included the overall detection rate of PCa, cancer detection rate per core, and complications.ResultsThe clinically significant cancer detection rate, overall cancer detection rate, and "per patient" did not significantly differ between RA-TB and RA-SB [OR = 1.02 (95% CI 0.83; 1.26), p = 0.05, I2 = 62% and OR = 0.95 (95% CI 0.78; 1.17), p = 0.17, I2 = 40%, respectively]. There were no differences in the clinically insignificant cancer detection rate "per patient" between RA-TB and RA-SB [OR = 0.81 (95% CI 0.54; 1.21), p = 0.31, I2 = 0%]. RA-TB had a significantly higher cancer detection rate "per core" [OR = 3.01 (95% CI 2.77; 3.27), p < 0.0001, I2 = 96%].ConclusionRA-TB and RA-SB are both technically feasible and have comparable clinical significance and overall PCa detection rates.
| S-EPMC9954527 | biostudies-literature