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  • Frontiers Media SA  (39)
  • 1
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    Data_Sheet_1.docx
    Frontiers Media SA ; 2022
    In:  Frontiers in Neurology Vol. 13 ( 2022-8-25)
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    In: Frontiers in Neurology, Frontiers Media SA, Vol. 13 ( 2022-8-25)
    Abstract: The role of three-dimensional (3D) TOF-MRA in patients with cognitive impairment is not well established. We evaluated the diagnostic yield of 3D TOF-MRA for detecting incidental extra- or intracranial artery stenosis and intracranial aneurysm in this patient group. Methods This retrospective study included patients with cognitive impairment undergoing our brain MRI protocol from January 2013 to February 2020. The diagnostic yield of TOF-MRA for detecting incidental vascular lesions was calculated. Patients with positive TOF-MRA results were reviewed to find whether additional treatment was performed. Logistic regression analysis was conducted to identify the clinical risk factors for positive TOF-MRA findings. Results In total, 1,753 patients (mean age, 70.2 ± 10.6 years; 1,044 women) were included; 199 intracranial aneurysms were detected among 162 patients (9.2%, 162/1,753). A 3D TOF-MRA revealed significant artery stenoses ( & gt;50% stenosis) in 162 patients (9.2%, 162/1,753). The overall diagnostic yield of TOF-MRA was 16.8% (294/1,753). Among them, 92 patients (31.3%, 92/294) underwent either medical therapy, endovascular intervention, or surgery. In total, eighty-one patients with stenosis were prescribed with either antiplatelet medications or lipid-lowering agent. In total, fifteen patients (aneurysm: 11 patients, stenosis: 4 patients) were further treated with endovascular intervention or surgery. Thus, the “number needed to scan” was 19 for identifying one patient requiring treatment. Multivariate logistic regression analysis showed that being female (odds ratio [OR] 2.05) and old age (OR 1.04) were the independent risk factors for intracranial aneurysm; being male (OR 1.52), old age (OR 1.06), hypertension (OR 1.78), and ischemic heart disease history (OR 2.65) were the independent risk factors for significant artery stenosis. Conclusions Our study demonstrated the potential benefit of 3D TOF-MRA, given that it showed high diagnostic yield for detecting vascular lesions in patients with cognitive impairment and the considerable number of these lesions required further treatment. A 3D TOF-MRA may be included in the routine MR protocol for the work-up of this patient population, especially in older patients and patients with vascular risk factors.
    Type of Medium: Online Resource
    ISSN: 1664-2295
    URL: Article
    URL: Article
    DOI: 10.3389/fneur.2022.958037
    DOI: 10.3389/fneur.2022.958037.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
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  • 2
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    Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg
    Frontiers Media SA ; 2023
    In:  Frontiers in Neurology Vol. 14 ( 2023-9-1)
    Details
    In: Frontiers in Neurology, Frontiers Media SA, Vol. 14 ( 2023-9-1)
    Abstract: To develop and validate a deep learning-based automatic segmentation model for assessing intracranial volume (ICV) and to compare the accuracy determined by NeuroQuant (NQ), FreeSurfer (FS), and SynthSeg. Materials and methods This retrospective study included 60 subjects [30 Alzheimer’s disease (AD), 21 mild cognitive impairment (MCI), 9 cognitively normal (CN)] from a single tertiary hospital for the training and validation group (50:10). The test group included 40 subjects (20 AD, 10 MCI, 10 CN) from the ADNI dataset. We propose a robust ICV segmentation model based on the foundational 2D UNet architecture trained with four types of input images (both single and multimodality using scaled or unscaled T1-weighted and T2-FLAIR MR images). To compare with our model, NQ, FS, and SynthSeg were also utilized in the test group. We evaluated the model performance by measuring the Dice similarity coefficient (DSC) and average volume difference. Results The single-modality model trained with scaled T1-weighted images showed excellent performance with a DSC of 0.989 ± 0.002 and an average volume difference of 0.46% ± 0.38%. Our multimodality model trained with both unscaled T1-weighted and T2-FLAIR images showed similar performance with a DSC of 0.988 ± 0.002 and an average volume difference of 0.47% ± 0.35%. The overall average volume difference with our model showed relatively higher accuracy than NQ (2.15% ± 1.72%), FS (3.69% ± 2.93%), and SynthSeg (1.88% ± 1.18%). Furthermore, our model outperformed the three others in each subgroup of patients with AD, MCI, and CN subjects. Conclusion Our deep learning-based automatic ICV segmentation model showed excellent performance for the automatic evaluation of ICV.
    Type of Medium: Online Resource
    ISSN: 1664-2295
    URL: Article
    DOI: 10.3389/fneur.2023.1221892
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
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  • 3
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    Corrigendum: Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg
    Frontiers Media SA ; 2023
    In:  Frontiers in Neurology Vol. 14 ( 2023-11-29)
    Details
    In: Frontiers in Neurology, Frontiers Media SA, Vol. 14 ( 2023-11-29)
    Type of Medium: Online Resource
    ISSN: 1664-2295
    URL: Article
    DOI: 10.3389/fneur.2023.1334962
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
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  • 4
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    Table_1.docx
    Frontiers Media SA ; 2024
    In:  Frontiers in Oncology Vol. 14 ( 2024-2-28)
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    In: Frontiers in Oncology, Frontiers Media SA, Vol. 14 ( 2024-2-28)
    Abstract: Atezolizumab+bevacizumab (AB) and lenvatinib have been proposed as first-line treatment options for patients with advanced hepatocellular carcinoma (HCC), but comparative efficacy and associated factors are controversial. Materials and methods This real-world multicenter study analysed patients with HCC who received AB (n=169) or lenvatinib (n=177). Results First, 1:1 propensity score matching (PSM) was performed, resulting in 141 patients in both the AB and lenvatinib groups. After PSM, overall survival (OS) was better in the AB group than in the lenvatinib group [hazard ratio (HR)=0.642, P=0.009], but progression-free survival (PFS) did not vary between the two groups (HR=0.817, P=0.132). Objective response rate (ORR) was also similar between AB and lenvatinib (34.8% vs. 30.8%, P=0.581). In a subgroup of patients with objective responses (OR, n=78), OS (HR=0.364, P=0.012) and PFS (HR=0.536, P=0.019) were better in the AB group (n=41) than in the lenvatinib group (n=37). Time-to-progression from time of OR was also better in the AB group (HR=0.465, P=0.012). Importantly, residual liver function was a significant factor related to OS in both treatments. Child-Pugh score following cessation of the respective treatments was better in the AB group (n=105) than in the lenvatinib group (n=126) (median 6 versus 7, P=0.008), and proportion of salvage treatment was also higher in the AB group (52.4% versus 38.9%, P=0.047). When we adjusted for residual liver function or salvage treatment, there was no difference in OS between the two treatments. Conclusion Our study suggests that residual liver function and subsequent salvage treatments are major determinants of clinical outcomes in patients treated with AB and lenvatinib; these factors should be considered in future comparative studies.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fonc.2024.1372007
    DOI: 10.3389/fonc.2024.1372007.s001
    DOI: 10.3389/fonc.2024.1372007.s002
    DOI: 10.3389/fonc.2024.1372007.s003
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2024
    detail.hit.zdb_id: 2649216-7
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  • 5
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    Table_1.docx
    Frontiers Media SA ; 2022
    In:  Frontiers in Neurology Vol. 13 ( 2022-8-22)
    Details
    In: Frontiers in Neurology, Frontiers Media SA, Vol. 13 ( 2022-8-22)
    Abstract: Identifying biomarkers for predicting progression to dementia in patients with mild cognitive impairment (MCI) is crucial. To this end, the comprehensive visual rating scale (CVRS), which is based on magnetic resonance imaging (MRI), was developed for the assessment of structural changes in the brains of patients with MCI. This study aimed to investigate the use of the CVRS score for predicting dementia in patients with MCI over a 2-year follow-up period using various machine learning (ML) algorithms. Methods We included 197 patients with MCI who were followed up more than once. The data used for this study were obtained from the Japanese-Alzheimer's Disease Neuroimaging Initiative study. We assessed all the patients using their CVRS scores, cortical thickness data, and clinical data to determine their progression to dementia during a follow-up period of over 2 years. ML algorithms, such as logistic regression, random forest (RF), XGBoost, and LightGBM, were applied to the combination of the dataset. Further, feature importance that contributed to the progression from MCI to dementia was analyzed to confirm the risk predictors among the various variables evaluated. Results Of the 197 patients, 108 (54.8%) showed progression from MCI to dementia. Tree-based classifiers, such as XGBoost, LightGBM, and RF, achieved relatively high performance. In addition, the prediction models showed better performance when clinical data and CVRS score (accuracy 0.701–0.711) were used than when clinical data and cortical thickness (accuracy 0.650–0.685) were used. The features related to CVRS helped predict progression to dementia using the tree-based models compared to logistic regression. Conclusions Tree-based ML algorithms can predict progression from MCI to dementia using baseline CVRS scores combined with clinical data.
    Type of Medium: Online Resource
    ISSN: 1664-2295
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fneur.2022.906257
    DOI: 10.3389/fneur.2022.906257.s001
    DOI: 10.3389/fneur.2022.906257.s002
    DOI: 10.3389/fneur.2022.906257.s003
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
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  • 6
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    DataSheet_1.docx
    Frontiers Media SA ; 2022
    In:  Frontiers in Oncology Vol. 12 ( 2022-11-21)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 12 ( 2022-11-21)
    Abstract: Based on the immunologic effects of anti-cancer treatment and their therapeutic implications, we evaluated radiotherapy (RT)-induced dynamic alterations in programmed death-1 (PD-1)/PD ligand-1 (PD-L1) expression profiles. Methods Local RT with 2 Gy × 5 or 7.5 Gy × 1 was administered to the CT26 mouse model. Thereafter, tumors were resected and evaluated at the following predefined timepoints according to radiation response status: baseline, early (immediately after RT), middle (beginning of tumor shrinkage), late (stable status with RT effect), and progression (tumor regrowth). PD-1/PD-L1 activity and related immune cell profiles were quantitatively assessed. Results RT upregulated PD-L1 expression in tumor cells from the middle to late phase; however, the levels subsequently decreased to levels comparable to baseline in the progression phase. RT with 2 Gy × 5 induced a higher frequency of PD-L1+ myeloid-derived suppressor cells, with a lesser degree of tumor regression, compared to 7.5 Gy. The proportion of PD-1+ and interferon (IFN)-γ+CD8α T cells continued to increase. The frequency of splenic PD-1+CD8+ T cells was markedly elevated, and was sustained longer with 2 Gy × 5. Based on the transcriptomic data, RT stimulated the transcription of immune-related genes, leading to sequentially altered patterns. Discussion The dynamic alterations in PD-1/PD-L1 expression level were observed according to the time phases of tumor regression. This study suggests the influence of tumor cell killing and radiation dosing strategy on the tumor immune microenvironment.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    URL: Article
    DOI: 10.3389/fonc.2022.989190
    DOI: 10.3389/fonc.2022.989190.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
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  • 7
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    Table_1.DOCX
    Frontiers Media SA ; 2021
    In:  Frontiers in Neurology Vol. 12 ( 2021-7-1)
    Details
    In: Frontiers in Neurology, Frontiers Media SA, Vol. 12 ( 2021-7-1)
    Abstract: Background and Aims: Systemic inflammation is associated with an increased risk of cognitive impairment and dementia, but the associations between them in stroke patients are less clear. We examined the impact of systemic inflammation represented as the neutrophil-lymphocyte ratio (NLR) on the development of post-stroke cognitive impairment (PSCI) and domain-specific cognitive outcomes 3-month after ischemic stroke. Methods: Using prospective stroke registry data, we consecutively enrolled 345 participants with ischemic stroke whose cognitive functions were evaluated 3-month after stroke. Their cognition was assessed with the Korean version of the Vascular Cognitive Impairment Harmonization Standards and the Korean-Mini Mental Status Examination. PSCI was defined as a z-score of & lt; -2 standard deviations for age, sex, and education adjusted means in at least one cognitive domain. The participants were categorized into five groups according to the quintiles of NLR (lowest NLR, Q1). The cross-sectional association between NLR and PSCI was assessed using multiple logistic regression, adjusting for age, sex, education, vascular risk factors, and stroke type. Results: A total of 345 patients were enrolled. The mean age was 63.0 years and the median NIHSS score and NLR were 2 [1–4] and 2.26 [1.65–2.91] , respectively. PSCI was identified in 71 (20.6%) patients. NLR was a significant predictor for PSCI both as a continuous variable (adjusted OR, 1.14; 95% CI, 1.00–1.31) and as a categorical variable (Q5, adjusted OR, 3.26; 95% CI, 1.17–9.08). Patients in the Q5 group (NLR ≥ 3.80) showed significantly worse performance in global cognition and in visuospatial and memory domains. Conclusions: NLR in the acute stage of ischemic stroke was independently associated with PSCI at 3 months after stroke, and high NLR was specifically associated with cognitive dysfunction in the memory and visuospatial domains. Thus, systemic inflammation may be a modifiable risk factor that may influence cognitive outcomes after stroke.
    Type of Medium: Online Resource
    ISSN: 1664-2295
    URL: Article
    URL: Article
    DOI: 10.3389/fneur.2021.693318
    DOI: 10.3389/fneur.2021.693318.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
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  • 8
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    Table_2.DOCX
    Frontiers Media SA ; 2020
    In:  Frontiers in Neurology Vol. 11 ( 2020-1-31)
    Details
    In: Frontiers in Neurology, Frontiers Media SA, Vol. 11 ( 2020-1-31)
    Type of Medium: Online Resource
    ISSN: 1664-2295
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fneur.2020.00037
    DOI: 10.3389/fneur.2020.00037.s001
    DOI: 10.3389/fneur.2020.00037.s002
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2020
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  • 9
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    Table_1.DOCX
    Frontiers Media SA ; 2020
    In:  Frontiers in Neurology Vol. 11 ( 2020-12-2)
    Details
    In: Frontiers in Neurology, Frontiers Media SA, Vol. 11 ( 2020-12-2)
    Abstract: Background: Although controversial, homocysteine (Hcy) and lipid parameters have been associated with particular stroke subtypes. However, there are limited studies concerning the relationship between Hcy and lipid levels in acute ischemic stroke (AIS). We evaluated the impact of Hcy levels on lipid profiles in terms of specific stroke subtypes. Methods: A total of 2,324 patients with first-ever AIS were recruited from two hospitals in South Korea. The exclusion criteria were as follows: (a) pre-stroke modified Rankin scale (mRS) ≥ 1, (b) undetermined or other stroke etiology, and (c) absence of Hcy data. Among the 1,580 eligible patients, the Hcy level was divided into tertile groups. Logistic regression was used to assess association of Hcy levels with lipid levels by stroke subtypes. Results: Significant downward trends in total cholesterol, low-density lipoprotein (LDL), and high-density lipoprotein (HDL) were only observed in patients with small vessel occlusion (SVO) as Hcy increased. In logistic regression analysis, while in patients with SVO subtype, the highest level of Hcy tertiles (OR = 1.648, 95% CI = 1.047–2.594) was associated with the lower HDL level (≤40 mg/dL), the significance disappeared in patients with LAA and CE subtypes. Conclusion: Although our study does not demonstrate causal relationship, we suggest that Hcy might play a mediating role between HDL and SVO stroke development. To clarify the role of Hcy on AIS, this study will provide academic support for designing future research.
    Type of Medium: Online Resource
    ISSN: 1664-2295
    URL: Article
    URL: Article
    DOI: 10.3389/fneur.2020.565506
    DOI: 10.3389/fneur.2020.565506.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2020
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  • 10
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    Table_1.docx
    Frontiers Media SA ; 2023
    In:  Frontiers in Aging Neuroscience Vol. 15 ( 2023-9-28)
    Details
    In: Frontiers in Aging Neuroscience, Frontiers Media SA, Vol. 15 ( 2023-9-28)
    Abstract: More than half of patients with acute ischemic stroke develop post-stroke cognitive impairment (PSCI), a significant barrier to future neurological recovery. Thus, predicting cognitive trajectories post-AIS is crucial. Our primary objective is to determine whether brain network properties from electroencephalography (EEG) can predict post-stroke cognitive function using machine learning approach. Methods We enrolled consecutive stroke patients who underwent both EEG during the acute stroke phase and cognitive assessments 3 months post-stroke. We preprocessed acute stroke EEG data to eliminate low-quality epochs, then performed independent component analysis and quantified network characteristics using iSyncBrain®. Cognitive function was evaluated using the Montreal cognitive assessment (MoCA). We initially categorized participants based on the lateralization of their lesions and then developed machine learning models to predict cognitive status in the left and right hemisphere lesion groups. Results Eighty-seven patients were included, and the accuracy of lesion laterality prediction using EEG attributes was 97.0%. In the left hemispheric lesion group, the network attributes of the theta band were significantly correlated with MoCA scores, and higher global efficiency, clustering coefficient, and lower characteristic path length were associated with higher MoCA scores. Most features related to cognitive scores were selected from the frontal lobe. The predictive powers ( R -squared) were 0.76 and 0.65 for the left and right stroke groups, respectively. Conclusion Estimating EEG-based network properties in the acute phase of ischemic stroke through a machine learning model has a potential to predict cognitive outcomes after ischemic stroke.
    Type of Medium: Online Resource
    ISSN: 1663-4365
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fnagi.2023.1238274
    DOI: 10.3389/fnagi.2023.1238274.s001
    DOI: 10.3389/fnagi.2023.1238274.s002
    DOI: 10.3389/fnagi.2023.1238274.s003
    DOI: 10.3389/fnagi.2023.1238274.s004
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2558898-9
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