Standardized evaluation of the extent of resection in glioblastoma with automated early post-operative segmentation.

Standard treatment of patients with glioblastoma includes surgical resection of the tumor. The extent of resection (EOR) achieved during surgery significantly impacts prognosis and is used to stratify patients in clinical trials. In this study, we developed a U-Net-based deep-learning model to segment contrast-enhancing tumor on post-operative MRI exams taken within 72 h of resection surgery and used these segmentations to classify the EOR as either maximal or submaximal.

Constructing personalized characterizations of structural brain aberrations in patients with dementia using explainable artificial intelligence.

Deep learning approaches for clinical predictions based on magnetic resonance imaging data have shown great promise as a translational technology for diagnosis and prognosis in neurological disorders, but its clinical impact has been limited. This is partially attributed to the opaqueness of deep learning models, causing insufficient understanding of what underlies their decisions. To overcome this, we trained convolutional neural networks on structural brain scans to differentiate dementia patients from healthy controls, and applied layerwise relevance propagation to procure individual-level explanations of the model predictions.

Radiological features of brain hemorrhage through automated segmentation from computed tomography in stroke and traumatic brain injury.

Introduction: Radiological assessment is necessary to diagnose spontaneous intracerebral hemorrhage (ICH) and traumatic brain injury intracranial hemorrhage (TBI-bleed). Artificial intelligence (AI) deep learning tools provide a means for decision support. This study evaluates the hemorrhage segmentations produced from three-dimensional deep learning AI model that was developed using non-contrast computed tomography (CT) imaging data external to the current study.

Diagnostic imaging strategies of acute intracerebral hemorrhage in European academic hospitals-a decision-making analysis.

Purpose: To evaluate and compare which factors are relevant to the diagnostic decision-making and imaging workup of intracerebral hemorrhages in large, specialized European centers.

Methods: Expert neuroradiologists from ten large, specialized centers (where endovascular stroke treatment is routinely performed) in nine European countries were selected in cooperation with the European Society of Neuroradiology (ESNR). The experts were asked to describe how and when they would investigate specific causes in a patient who presented with an acute, atraumatic, intracerebral hemorrhage for two given locations: (1) basal ganglia, thalamus, pons or cerebellum; (2) lobar hemorrhage.

Neuroimaging markers of dual impairment in cognition and physical performance following stroke: The Nor-COAST study.

Background: Cognitive decline and decline in physical performance are common after stroke. Concurrent impairments in the two domains are reported to give increased risk of dementia and functional decline. The concept of dual impairment of physical performance and cognition after stroke is poorly investigated.

Longitudinal brain age prediction and cognitive function after stroke.

Advanced age is associated with post-stroke cognitive decline. Machine learning based on brain scans can be used to estimate brain age of patients, and the corresponding difference from chronological age, the brain age gap (BAG), has been investigated in a range of clinical conditions, yet not thoroughly in post-stroke neurocognitive disorder (NCD). We aimed to investigate the association between BAG and post-stroke NCD over time.

Longitudinal Brain Changes After Stroke and the Association With Cognitive Decline.

Background: Cognitive impairment is common after stroke. So is cortical- and subcortical atrophy, with studies reporting more atrophy in the ipsilesional hemisphere than the contralesional hemisphere. The current study aimed to investigate the longitudinal associations between (I) lateralization of brain atrophy and stroke hemisphere, and (II) cognitive impairment and brain atrophy after stroke.

Is Frailty Index a better predictor than pre-stroke modified Rankin Scale for neurocognitive outcomes 3-months post-stroke?

Background: The prognostic value of frailty measures for post-stroke neurocognitive disorder (NCD) remains to be evaluated.

Aims: The aim of this study was to compare the predictive value of pre-stroke FI with pre-stroke modified Rankin Scale (mRS) for post-stroke cognitive impairment. Further, we explored the added value of including FI in prediction models for cognitive prognosis post-stroke.

Predicting the Emergence of Major Neurocognitive Disorder Within Three Months After a Stroke.

Neurocognitive disorder (NCD) is common after stroke, with major NCD appearing in about 10% of survivors of a first-ever stroke. We aimed to classify clinical- and imaging factors related to rapid development of major NCD 3 months after a stroke, so as to examine the optimal composition of factors for predicting rapid development of the disorder. We hypothesized that the prediction would mainly be driven by neurodegenerative as opposed to vascular brain changes.

Pre-stroke cognitive impairment is associated with vascular imaging pathology: a prospective observational study.

Background: Chronic brain pathology and pre-stroke cognitive impairment (PCI) is predictive of post-stroke dementia. The aim of the current study was to measure pre-stroke neurodegenerative and vascular disease burden found on brain MRI and to assess the association between pre-stroke imaging pathology and PCI, whilst also looking for potential sex differences.

Methods: This prospective brain MRI cohort is part of the multicentre Norwegian cognitive impairment after stroke (Nor-COAST) study.

Clinically accessible neuroimaging predictors of post-stroke neurocognitive disorder: a prospective observational study.

Background: Neurocognitive disorder (NCD) is common in stroke survivors. We aimed to identify clinically accessible imaging markers of stroke and chronic pathology that are associated with early post-stroke NCD.

Methods: We included 231 stroke survivors from the "Norwegian Cognitive Impairment after Stroke (Nor-COAST)" study who underwent a standardized cognitive assessment 3 months after the stroke.

Analysis of diffusion tensor imaging metrics for gliomas grading at 3 T.

Objectives: To assess the diagnostic accuracy of axial diffusivity (AD), radial diffusivity (RD), apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values derived from DTI for grading of glial tumors, and to estimate the correlation between DTI parameters and tumor grades.

Methods: Seventy-eight patients with glial tumors underwent DTI. AD, RD, ADC and FA values of tumor, peritumoral edema and contralateral normal-appearing white matter (NAWM) and AD, RD, ADC and FA ratios: lowest average AD, RD, ADC and FA values in tumor or peritumoral edema to AD, RD, ADC and FA of NAWM were calculated.

[High-grade gliomas in adults].

Background: High-grade glioma is a primary malignant brain tumour which affects about 200 Norwegian patients each year. Diagnosis and treatment of high-grade gliomas in adults has been reviewed.

Material And Methods: The article is based on recent literature retrieved through a non-systematic search in PubMed and the authors' experience with the patient group.

Measurements of diagnostic examination performance and correlation analysis using microvascular leakage, cerebral blood volume, and blood flow derived from 3T dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in glial tumor grading.

Introduction: To assess the diagnostic accuracy of microvascular leakage (MVL), cerebral blood volume (CBV) and blood flow (CBF) values derived from dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging (DSC-MR imaging) for grading of cerebral glial tumors, and to estimate the correlation between vascular permeability/perfusion parameters and tumor grades.

Methods: A prospective study of 79 patients with cerebral glial tumors underwent DSC-MR imaging. Normalized relative CBV (rCBV) and relative CBF (rCBF) from tumoral (rCBVt and rCBFt), peri-enhancing region (rCBVe and rCBFe), and the value in the tumor divided by the value in the peri-enhancing region (rCBVt/e and rCBFt/e), as well as MVL, expressed as the leakage coefficient K(2) were calculated.

Proton magnetic resonance spectroscopy in the distinction of high-grade cerebral gliomas from single metastatic brain tumors.

Background: Brain metastases and primary high-grade gliomas, including glioblastomas multiforme (GBM) and anaplastic astrocytomas (AA), may be indistinguishable by conventional magnetic resonance (MR) imaging. Identification of these tumors may have therapeutic consequences.

Purpose: To assess the value of MR spectroscopy (MRS) using short and intermediate echo time (TE) in differentiating solitary brain metastases and high-grade gliomas on the basis of differences in metabolite ratios in the intratumoral and peritumoral region.