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Frontiers in Psychology 2023Diffusion Tensor Imaging (DTI) indicators of different white matter (WM) fibers and brain region lesions for post-stroke aphasia (PSA) are inconsistent in existing...
INTRODUCTION
Diffusion Tensor Imaging (DTI) indicators of different white matter (WM) fibers and brain region lesions for post-stroke aphasia (PSA) are inconsistent in existing studies. Our study examines the consistency and differences between PSA tests performed with DTI. In addition, obtaining consistent and independent conclusions between studies was made possible by utilizing DTI in PSA assessment.
METHODS
In order to gather relevant studies using DTI for diagnosing PSA, we searched the Web of Science, PubMed, Embase, and CNKI databases. Based on the screening and evaluation of the included studies, the meta-analysis was used to conduct a quantitative analysis. Narrative descriptions were provided for studies that met the inclusion criteria but lacked data.
RESULTS
First, we reported on the left hemisphere. The meta-analysis showed that fractional anisotropy (FA) of the arcuate fasciculus (AF) and superior longitudinal fasciculus (SLF), inferior frontal-occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF), and uncinate fasciculus (UF) were decreased in the PSA group in comparison with the healthy controls ( < 0.00001). However, in the comparison of axial diffusivity (AD), there was no statistically significant difference in white matter fiber tracts in the dual-stream language model of the PSA group. Elevated radial diffusivity (RD) was seen only in the IFOF and ILF ( = 0.01; = 0.05). In the classic Broca's area, the FA of the PSA group was decreased ( < 0.00001) while the apparent diffusion coefficient was elevated ( = 0.03). Secondly, we evaluated the white matter fiber tracts in the dual-stream language model of the right hemisphere. The FA of the PSA group was decreased only in the IFOF ( = 0.001). AD was elevated in the AF and UF ( < 0.00001; PUF = 0.009). RD was elevated in the AF and UF ( = 0.01; = 0.003). The other fiber tracts did not undergo similar alterations.
CONCLUSION
In conclusion, DTI is vital for diagnosing PSA because it detects WM changes effectively, but it still has some limitations. Due to a lack of relevant language scales and clinical manifestations, diagnosing and differentiating PSA independently remain challenging.
SYSTEMATIC REVIEW REGISTRATION
https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=365897.
PubMed: 37790217
DOI: 10.3389/fpsyg.2023.1140588 -
Seminars in Perinatology Dec 2016Up to 35% of very preterm infants survive with neurodevelopmental impairments (NDI) such as cognitive deficits, cerebral palsy, and attention deficit disorder. Advanced... (Review)
Review
Up to 35% of very preterm infants survive with neurodevelopmental impairments (NDI) such as cognitive deficits, cerebral palsy, and attention deficit disorder. Advanced MRI quantitative tools such as brain morphometry, diffusion MRI, magnetic resonance spectroscopy, and functional MRI at term-equivalent age are ideally suited to improve current efforts to predict later development of disabilities. This would facilitate application of targeted early intervention therapies during the first few years of life when neuroplasticity is optimal. A systematic search and review identified 47 published studies of advanced MRI to predict NDI. Diffusion MRI and morphometry studies were the most commonly studied modalities. Despite several limitations, studies clearly showed that brain structural and metabolite biomarkers are promising independent predictors of NDI. Large representative multicenter studies are needed to validate these studies.
Topics: Brain; Developmental Disabilities; Fetal Organ Maturity; Humans; Infant, Extremely Premature; Infant, Newborn; Infant, Premature, Diseases; Infant, Very Low Birth Weight; Intensive Care, Neonatal; Magnetic Resonance Imaging; Neuroimaging; Predictive Value of Tests
PubMed: 27863706
DOI: 10.1053/j.semperi.2016.09.005 -
Scientific Reports Sep 2021Early prediction of treatment response in nasopharyngeal carcinoma is clinically relevant for optimizing treatment strategies. This meta-analysis was performed to... (Meta-Analysis)
Meta-Analysis
Early prediction of treatment response in nasopharyngeal carcinoma is clinically relevant for optimizing treatment strategies. This meta-analysis was performed to evaluate whether apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI) can predict treatment response of patients with nasopharyngeal carcinoma. A systematic search of PubMed-MEDLINE and Embase was performed to identify relevant original articles until July 22, 2021. We included studies which performed DWI for predicting locoregional treatment response in nasopharyngeal carcinoma treated with neoadjuvant chemotherapy, definitive chemoradiation, or radiation therapy. Hazard ratios were meta-analytically pooled using a random-effects model for the pooled estimates of overall survival, local relapse-free survival, distant metastasis-free survival and their 95% CIs. ADC showed a pooled sensitivity of 87% (95% CI 72-94%) and specificity of 70% (95% CI 56-80%) for predicting treatment response. Significant between-study heterogeneity was observed for both pooled sensitivity (I = 68.5%) and specificity (I = 92.2%) (P < 0.01). The pooled hazard ratios of low pretreatment ADC for assessing overall survival, local relapse-free survival, and distant metastasis-free survival were 1.42 (95% CI 1.09-1.85), 2.31 (95% CI 1.42-3.74), and 1.35 (95% CI 1.05-1.74), respectively. In patients with nasopharyngeal carcinoma, pretreatment ADC demonstrated good predictive performance for treatment response.
Topics: Chemoradiotherapy; Diffusion Magnetic Resonance Imaging; Disease-Free Survival; Feasibility Studies; Humans; Nasopharyngeal Carcinoma; Nasopharyngeal Neoplasms; Nasopharynx; Neoadjuvant Therapy; Neoplasm Recurrence, Local; Predictive Value of Tests; Prognosis; Risk Assessment
PubMed: 34556743
DOI: 10.1038/s41598-021-98508-5 -
CNS Neuroscience & Therapeutics Feb 2024Amyotrophic lateral sclerosis (ALS) is a progressive motor and extra-motor neurodegenerative disease. This systematic review aimed to examine MRI biomarkers and... (Review)
Review
BACKGROUND AND OBJECTIVE
Amyotrophic lateral sclerosis (ALS) is a progressive motor and extra-motor neurodegenerative disease. This systematic review aimed to examine MRI biomarkers and neuropsychological assessments of the hippocampal and parahippocampal regions in patients with ALS.
METHODS
A systematic review was conducted in the Scopus and PubMed databases for studies published between January 2000 and July 2023. The inclusion criteria were (1) MRI studies to assess hippocampal and parahippocampal regions in ALS patients, and (2) studies reporting neuropsychological data in patients with ALS.
RESULTS
A total of 46 studies were included. Structural MRI revealed hippocampal atrophy, especially in ALS-FTD, involving specific subregions (CA1, dentate gyrus). Disease progression and genetic factors impacted atrophy patterns. Diffusion tensor imaging (DTI) showed increased mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and decreased fractional anisotropy (FA) in the hippocampal tracts and adjacent regions, indicating loss of neuronal and white matter integrity. Functional MRI (fMRI) revealed reduced functional connectivity (FC) between the hippocampus, parahippocampus, and other regions, suggesting disrupted networks. Perfusion MRI showed hypoperfusion in parahippocampal gyri. Magnetic resonance spectroscopy (MRS) found changes in the hippocampus, indicating neuronal loss. Neuropsychological tests showed associations between poorer memory and hippocampal atrophy or connectivity changes. CA1-2, dentate gyrus, and fimbria atrophy were correlated with worse memory.
CONCLUSIONS
The hippocampus and the connected regions are involved in ALS. Hippocampal atrophy disrupted connectivity and metabolite changes correlate with cognitive and functional decline. Specific subregions can be particularly affected. The hippocampus is a potential biomarker for disease monitoring and prognosis.
Topics: Humans; Diffusion Tensor Imaging; Amyotrophic Lateral Sclerosis; Neurodegenerative Diseases; Frontotemporal Dementia; Magnetic Resonance Imaging; Hippocampus; Biomarkers; Neuropsychological Tests; Atrophy
PubMed: 38334254
DOI: 10.1111/cns.14578 -
RMD Open Nov 2023The course of systemic sclerosis-associated interstitial lung disease (SSc-ILD) is highly variable and different from continuously progressive idiopathic pulmonary...
The course of systemic sclerosis-associated interstitial lung disease (SSc-ILD) is highly variable and different from continuously progressive idiopathic pulmonary fibrosis (IPF). Most proposed definitions of progressive pulmonary fibrosis or SSc-ILD severity are based on the research data from patients with IPF and are not validated for patients with SSc-ILD. Our study aimed to gather the current evidence for severity, progression and outcomes of SSc-ILD. A systematic literature review to search for definitions of severity, progression and outcomes recorded for SSc-ILD was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines in Medline, Embase, Web of Science and Cochrane Library up to 1 August 2023. A total of 9054 papers were reviewed and 342 were finally included. The most frequent tools used for the definition of SSc-ILD progression and severity were combined changes of carbon monoxide diffusing capacity (DLCO) and forced vital capacity (FVC), isolated FVC or DLCO changes, high-resolution CT (HRCT) extension and composite algorithms including pulmonary function test, clinical signs and HRCT data. Mortality was the most frequently reported long-term event, both from all causes or ILD related. The studies presenting definitions of SSc-ILD 'progression', 'severity' and 'outcome' show a large heterogeneity. These results emphasise the need for developing a standardised, consensus definition of severe SSc-ILD, to link a disease specific definition of progression as a surrogate outcome for clinical trials and clinical practice.PROSPERO registration number CRD42022379254.Cite Now.
Topics: Humans; Lung; Lung Diseases, Interstitial; Scleroderma, Systemic; Patient Acuity; Disease Progression
PubMed: 37940340
DOI: 10.1136/rmdopen-2023-003426 -
NeuroImage. Clinical 2020Diffusion magnetic resonance imaging (dMRI) is an imaging technique which probes the random motion of water molecules in tissues and has been widely applied to... (Review)
Review
Diffusion magnetic resonance imaging (dMRI) is an imaging technique which probes the random motion of water molecules in tissues and has been widely applied to investigate changes in white matter microstructure in Alzheimer's Disease. This paper aims to systematically review studies that examined the effect of Alzheimer's risk genes on white matter microstructure. We assimilated findings from 37 studies and reviewed their diffusion pre-processing and analysis methods. Most studies estimate the diffusion tensor (DT) and compare derived quantitative measures such as fractional anisotropy and mean diffusivity between groups. Those with increased AD genetic risk are associated with reduced anisotropy and increased diffusivity across the brain, most notably the temporal and frontal lobes, cingulum and corpus callosum. Structural abnormalities are most evident amongst those with established Alzheimer's Disease. Recent studies employ signal representations and analysis frameworks beyond DT MRI but show that dMRI overall lacks specificity to disease pathology. However, as the field advances, these techniques may prove useful in pre-symptomatic diagnosis or staging of Alzheimer's disease.
Topics: Alzheimer Disease; Anisotropy; Brain; Diffusion Magnetic Resonance Imaging; Diffusion Tensor Imaging; Humans; White Matter
PubMed: 32758801
DOI: 10.1016/j.nicl.2020.102359 -
Digital Health 2022Image segmentation is an essential step in the analysis and subsequent characterisation of brain tumours through magnetic resonance imaging. In the literature,... (Review)
Review
BACKGROUND
Image segmentation is an essential step in the analysis and subsequent characterisation of brain tumours through magnetic resonance imaging. In the literature, segmentation methods are empowered by open-access magnetic resonance imaging datasets, such as the brain tumour segmentation dataset. Moreover, with the increased use of artificial intelligence methods in medical imaging, access to larger data repositories has become vital in method development.
PURPOSE
To determine what automated brain tumour segmentation techniques can medical imaging specialists and clinicians use to identify tumour components, compared to manual segmentation.
METHODS
We conducted a systematic review of 572 brain tumour segmentation studies during 2015-2020. We reviewed segmentation techniques using T1-weighted, T2-weighted, gadolinium-enhanced T1-weighted, fluid-attenuated inversion recovery, diffusion-weighted and perfusion-weighted magnetic resonance imaging sequences. Moreover, we assessed physics or mathematics-based methods, deep learning methods, and software-based or semi-automatic methods, as applied to magnetic resonance imaging techniques. Particularly, we synthesised each method as per the utilised magnetic resonance imaging sequences, study population, technical approach (such as deep learning) and performance score measures (such as Dice score).
STATISTICAL TESTS
We compared median Dice score in segmenting the whole tumour, tumour core and enhanced tumour.
RESULTS
We found that T1-weighted, gadolinium-enhanced T1-weighted, T2-weighted and fluid-attenuated inversion recovery magnetic resonance imaging are used the most in various segmentation algorithms. However, there is limited use of perfusion-weighted and diffusion-weighted magnetic resonance imaging. Moreover, we found that the U-Net deep learning technology is cited the most, and has high accuracy (Dice score 0.9) for magnetic resonance imaging-based brain tumour segmentation.
CONCLUSION
U-Net is a promising deep learning technology for magnetic resonance imaging-based brain tumour segmentation. The community should be encouraged to contribute open-access datasets so training, testing and validation of deep learning algorithms can be improved, particularly for diffusion- and perfusion-weighted magnetic resonance imaging, where there are limited datasets available.
PubMed: 35340900
DOI: 10.1177/20552076221074122 -
Arthritis and Rheumatism Sep 2013Pulmonary hypertension (PH) is a frequent and life-limiting complication of systemic sclerosis (SSc). However, data on survival rates and their evolution over time, as... (Meta-Analysis)
Meta-Analysis Review
OBJECTIVE
Pulmonary hypertension (PH) is a frequent and life-limiting complication of systemic sclerosis (SSc). However, data on survival rates and their evolution over time, as well as prognostic factors in SSc complicated by PH, are still conflicting. The aim of this study was to conduct a systematic review and meta-analysis of cohort studies to assess pooled survival and prognostic factors for survival in patients with SSc-associated PH.
METHODS
For this systematic review and meta-analysis, we searched the Medline and EMBase databases (January 1960 to January 2012). All cohort studies in which survival and/or prognostic factors for SSc-associated PH were reported were included in the analysis. We calculated the pooled survival rates and analyzed their evolution over time and identified prognostic factors for survival.
RESULTS
Twenty-two studies were included, representing a total of 2,244 patients with SSc-associated PH. The pooled 1-, 2-, and 3-year survival rates were 81% (95% confidence interval [95% CI] 79-84%), 64% (95% CI 59-69%), and 52% (95% CI 47-58%), respectively. Meta-regression did not reveal a significant change in survival over time, while baseline hemodynamic measures of PH severity were significantly correlated with survival. In patients with SSc complicated by pulmonary arterial hypertension (PAH), age, male sex, diffusing capacity for carbon monoxide (DLCO), pericardial effusion, and the parameters classically associated with the severity of idiopathic PAH, including the 6-minute walk distance, mean pulmonary artery pressure, cardiac index, and right atrial pressure, were significant prognostic factors. DLCO and pericardial effusion were the only prognostic factors in patients with interstitial lung disease-related PH.
CONCLUSION
Our meta-analysis revealed a poor pooled 3-year survival rate of 52% in patients with SSc-associated PH. Baseline hemodynamic measures of PAH severity, but not the period of time during which patients were included in the studies, correlated significantly with survival in patients with SSc-associated PAH. All of the prognostic factors typically observed in idiopathic PAH, including the 6-minute walk distance and right atrial pressure, were also prognostic factors in SSc-associated PAH.
Topics: Age Factors; Female; Humans; Hypertension, Pulmonary; Male; Prognosis; Scleroderma, Systemic; Sex Factors; Survival Rate
PubMed: 23740572
DOI: 10.1002/art.38029 -
Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics-A Systematic Review.Cancers Oct 2020Despite advances in surgical and medical treatment of glioblastoma (GBM), the medium survival is about 15 months and varies significantly, with occasional longer... (Review)
Review
Despite advances in surgical and medical treatment of glioblastoma (GBM), the medium survival is about 15 months and varies significantly, with occasional longer survivors and individuals whose tumours show a significant response to therapy with respect to others. Diffusion MRI can provide a quantitative assessment of the intratumoral heterogeneity of GBM infiltration, which is of clinical significance for targeted surgery and therapy, and aimed at improving GBM patient survival. So, the aim of this systematic review is to assess the role of diffusion MRI metrics in predicting survival of patients with GBM. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a systematic literature search was performed to identify original articles since 2010 that evaluated the association of diffusion MRI metrics with overall survival (OS) and progression-free survival (PFS). The quality of the included studies was evaluated using the QUIPS tool. A total of 52 articles were selected. The most examined metrics were associated with the standard Diffusion Weighted Imaging (DWI) (34 studies) and Diffusion Tensor Imaging (DTI) models (17 studies). Our findings showed that quantitative diffusion MRI metrics provide useful information for predicting survival outcomes in GBM patients, mainly in combination with other clinical and multimodality imaging parameters.
PubMed: 33020420
DOI: 10.3390/cancers12102858 -
Global Spine Journal Jun 2024Systematic review.
STUDY DESIGN
Systematic review.
OBJECTIVE
Degenerative cervical myelopathy (DCM) is a common spinal cord disorder necessitating surgery. We aim to explore how effectively diffusion tensor imaging (DTI) can distinguish DCM from healthy individuals and assess the relationship between DTI metrics and symptom severity.
METHODS
We included studies with adult DCM patients who had not undergone decompressive surgery and implemented correlation analyses between DTI parameters and severity, or compared healthy controls and DCM patients.
RESULTS
57 studies were included in our meta-analysis. At the maximal compression (MC) level, fractional anisotropy (FA) exhibited lower values in DCM patients, while apparent diffusion coefficient (ADC), mean diffusivity (MD), and radial diffusivity (RD) were notably higher in the DCM group. Moreover, our investigation into the diagnostic utility of DTI parameters disclosed high sensitivity, specificity, and area under the curve values for FA (.84, .80, .83 respectively) and ADC (.74, .84, .88 respectively). Additionally, we explored the correlation between DTI parameters and myelopathy severity, revealing a significant correlation of FA (.53, 95% CI:0.40 to .65) at MC level with JOA/mJOA scores.
CONCLUSION
Current guidelines for DCM suggest decompressive surgery for both mild and severe cases. However, they lack clear recommendations on which mild DCM patients might benefit from conservative treatment vs immediate surgery. ADC's role here could be pivotal, potentially differentiating between healthy individuals and DCM. While it may not correlate with symptom severity, it might predict surgical outcomes, making it a valuable imaging biomarker for clearer management decisions in mild DCM.
PubMed: 38877604
DOI: 10.1177/21925682241263792