Introduction: Glioblastoma is the most common malignant primary brain tumour with a median overall survival of 12-15 months (range 6-17 months), even with maximal treatment involving debulking neurosurgery and adjuvant concomitant chemoradiotherapy. The use of postoperative imaging to detect progression is of high importance to clinicians and patients, but currently, the optimal follow-up schedule is yet to be defined. It is also unclear how adhering to National Institute for Health and Care Excellence (NICE) guidelines-which are based on general consensus rather than evidence-affects patient outcomes such as progression-free and overall survival. The primary aim of this study is to assess MRI monitoring practice after surgery for glioblastoma, and to evaluate its association with patient outcomes.
Methods And Analysis: ImagiNg Timing aftER surgery for glioblastoma: an eVALuation of practice in Great Britain and Ireland is a retrospective multicentre study that will include 450 patients with an operated glioblastoma, treated with any adjuvant therapy regimen in the UK and Ireland. Adult patients ≥18 years diagnosed with glioblastoma and undergoing surgery between 1 August 2018 and 1 February 2019 will be included. Clinical and radiological scanning data will be collected until the date of death or date of last known follow-up. Anonymised data will be uploaded to an online Castor database. Adherence to NICE guidelines and the effect of being concordant with NICE guidelines will be identified using descriptive statistics and Kaplan-Meier survival analysis.
Ethics And Dissemination: Each participating centre is required to gain local institutional approval for data collection and sharing. Formal ethical approval is not required since this is a service evaluation. Results of the study will be reported through peer-reviewed presentations and articles, and will be disseminated to participating centres, patients and the public.
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http://dx.doi.org/10.1136/bmjopen-2022-063043 | DOI Listing |
Retin Cases Brief Rep
December 2024
Department of Ophthalmology, Hospital de Clínicas, University of Buenos Aires, Buenos Aires, Argentina.
Purpose: to report optical coherence tomography angiography findings in syphilitic outer retinopathy, a singular clinical manifestation of ocular syphilis.
Methods: case report.
Results: Multimodal imaging including optical coherence tomography angiography was performed in a patient presenting syphilitic outer retinopathy.
Background: Early detection and accurate forecasting of AD progression are crucial for timely intervention and management. This study leverages multi-modal data, including MRI scans, brain volumetrics, and clinical notes, utilizing Machine Learning (ML), Deep Learning (DL) and a range of ensemble methods to enhance the forecasting accuracy of Alzheimer's disease.
Method: We utilize the OASIS-3 longitudinal dataset, tracking 1,098 patients over 30 years.
Alzheimers Dement
December 2024
Douglas Research Centre/ McGill University, Montreal, QC, Canada.
Background: Altered neuronal timing and synchrony are biomarkers for Alzheimer's disease (AD) and correlate with memory impairments. Electrical stimulation of the fornix, the main fibre bundle connecting the hippocampus to the septum, has emerged as a potential intervention to restore network synchrony and memory performance in human AD and mouse models. However, electrical stimulation is non-specific and may partially explain why fornix stimulation in AD patients has yielded mixed results.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
University of California San Francisco, San Francisco, CA, USA.
Background: Neural circuit hyperexcitability and impaired excitation-to-inhibition (E/I) activity is believed to be a key contributor to synaptic and network degeneration in Alzheimer's disease (AD). Extensive preclinical research on transgenic animal models of AD have demonstrated neuronal and circuit level E/I imbalance mediated by amyloid-beta (Aβ) and tau proteins. Synaptic and network deficits are also integral changes of aging.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
The Ningbo Institute of Industrial Technology (CNITECH) of the Chinese Academy of Sciences (CAS), Ningbo, China.
Background: Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline and memory loss. Early and accurate diagnosis of AD is crucial for patient information, advance planning, and potentially effective intervention and treatment. The integration of machine learning techniques with brain connectome graphs, encompassing both structural and functional brain connectomes, can enhance the accuracy and efficiency of AD diagnosis.
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