Radiomics-Derived Brain Age Predicts Functional Outcome After Acute Ischemic Stroke

Machine Learning Assisted Stroke Prediction in Mechanical Circulatory Support: Predictive Role of Systemic Mitochondrial Dysfunction

Stroke continues to be a major adverse event in advanced congestive heart failure (CHF) patients after continuous-flow left ventricular assist device (CF-LVAD) implantation. Abnormalities in mitochondrial oxidative phosphorylation (OxPhos) have been critically implicated in the pathogenesis of neurodegenerative diseases and cerebral ischemia. We hypothesize that prior stroke may be associated with systemic mitochondrial OxPhos abnormalities, and impaired more in post-CF-LVAD patients with risk of developing new stroke. We studied 50 CF-LVAD patients (25 with prior stroke, 25 without); OxPhos complex proteins (complex I [C.I]-complex V [C.V]) were measured in blood leukocytes. Both at baseline (pre-CF-LVAD) and postoperatively (post-CF-LVAD), the prior-stroke group had significantly lower C.I, complex II (C.II), complex IV (C.IV), and C.V proteins when compared to the no-prior-stroke group. Oxidative phosphorylation proteins were significantly decreased in prior-stroke group at post-CF-LVAD compared to pre-CF-LVAD. Machine learning Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest modeling identified six prognostic factors that predicted postoperative stroke with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.93. Oxidative phosphorylation protein reduction appeared to be associated with the new stroke after implantation. Our study found for the first time the existence of mitochondrial dysfunction at the peripheral level in CHF patients with prior ischemic stroke even before CF-LVAD implantation. The changes in OxPhos protein expression could serve as biomarkers in predicting new post-CF-LVAD strokes.

Acceleration of brain aging after small-volume infarcts

Introduction

Previous studies have shown that stroke patients exhibit greater neuroimaging-derived biological "brain age" than control subjects. This difference, known as the brain age gap (BAG), is calculated by comparing the chronological age with predicted brain age and is used as an indicator of brain health and aging. However, whether stroke accelerates the process of brain aging in patients with small-volume infarcts has not been established. By utilizing longitudinal data, we aimed to investigate whether small-volume infarctions can significantly increase the BAG, indicating accelerated brain aging.

Methods

A total of 123 stroke patients presenting with small-volume infarcts were included in this retrospective study. The brain age model was trained via established protocols within the field of machine learning and the structural features of the brain from our previous study. We used t-tests and regression analyses to assess longitudinal brain age changes after stroke and the associations between brain age, acute stroke severity, and poststroke outcome factors.

Results

Significant brain aging occurred between the initial and 6-month follow-ups, with a mean increase in brain age of 1.04 years (t = 3.066, p < 0.05). Patients under 50 years of age experienced less aging after stroke than those over 50 years of age (p = 0.245). Additionally, patients with a National Institute of Health Stroke Scale score >3 at admission presented more pronounced adverse effects on brain aging, even after adjusting for confounders such as chronological age, sex, and total intracranial volume (F 1,117 = 7.339, p = 0.008, η 2 = 0.059). There were significant differences in the proportional brain age difference at 6 months among the different functional outcome groups defined by the Barthel Index (F 2,118 = 4.637, p = 0.012, η 2 = 0.073).

Conclusion

Stroke accelerates the brain aging process, even in patients with relatively small-volume infarcts. This phenomenon is particularly accentuated in elderly patients, and both stroke severity and poststroke functional outcomes are closely associated with accelerated brain aging. Further studies are needed to explore the mechanisms underlying the accelerated brain aging observed in stroke patients, with a particular focus on the structural alterations and plasticity of the brain following minor strokes.

Markers of Biological Brain Aging Mediate Effects of Vascular Risk Factors on Cognitive and Motor Functions: A Multivariate Imaging Analysis of 40,579 Individuals

The increasing global life expectancy brings forth challenges associated with age-related cognitive and motor declines. To better understand underlying mechanisms, we investigated the connection between markers of biological brain aging based on magnetic resonance imaging (MRI), cognitive and motor performance, as well as modifiable vascular risk factors, using a large-scale neuroimaging analysis in 40,579 individuals of the population-based UK Biobank and Hamburg City Health Study. Employing partial least squares correlation analysis (PLS), we investigated multivariate associative effects between three imaging markers of biological brain aging - relative brain age, white matter hyperintensities of presumed vascular origin, and peak-width of skeletonized mean diffusivity - and multi-domain cognitive test performances and motor test results. The PLS identified a latent dimension linking higher markers of biological brain aging to poorer cognitive and motor performances, accounting for 94.7% of shared variance. Furthermore, a mediation analysis revealed that biological brain aging mediated the relationship of vascular risk factors - including hypertension, glucose, obesity, and smoking - to cognitive and motor function. These results were replicable in both cohorts. By integrating multi-domain data with a comprehensive methodological approach, our study contributes evidence of a direct association between vascular health, biological brain aging, and functional cognitive as well as motor performance, emphasizing the need for early and targeted preventive strategies to maintain cognitive and motor independence in aging populations.

Predicting brain age gap with radiomics and automl: A Promising approach for age-Related brain degeneration biomarkers

The Brain Age Gap (BAG), which refers to the difference between chronological age and predicted neuroimaging age, is proposed as a potential biomarker for age-related brain degeneration. However, existing brain age prediction models usually rely on a single marker and can not discover meaningful hidden information in radiographic images. This study focuses on the application of radiomics, an advanced imaging analysis technique, combined with automated machine learning to predict BAG. Our methods achieve a promising result with a mean absolute error of 1.509 using the Alzheimer's Disease Neuroimaging Initiative dataset. Furthermore, we find that the hippocampus and parahippocampal gyrus play a significant role in predicting age with interpretable method called SHapley Additive exPlanations. Additionally, our investigation of age prediction discrepancies between patients with Alzheimer's disease (AD) and those with mild cognitive impairment (MCI) reveals a notable correlation with clinical cognitive assessment scale scores. This suggests that BAG has the potential to serve as a biomarker to support the diagnosis of AD and MCI. Overall, this study presents valuable insights into the application of neuroimaging models in the diagnosis of neurodegenerative diseases.

Magnetic resonance imaging-based deep learning imaging biomarker for predicting functional outcomes after acute ischemic stroke

PURPOSE

Clinical risk scores are essential for predicting outcomes in stroke patients. The advancements in deep learning (DL) techniques provide opportunities to develop prediction applications using magnetic resonance (MR) images. We aimed to develop an MR-based DL imaging biomarker for predicting outcomes in acute ischemic stroke (AIS) and evaluate its additional benefit to current risk scores.

METHOD

This study included 3338 AIS patients. We trained a DL model using deep neural network architectures on MR images and radiomics to predict poor functional outcomes at three months post-stroke. The DL model generated a DL score, which served as the DL imaging biomarker. We compared the predictive performance of this biomarker to five risk scores on a holdout test set. Additionally, we assessed whether incorporating the imaging biomarker into the risk scores improved the predictive performance.

RESULTS

The DL imaging biomarker achieved an area under the receiver operating characteristic curve (AUC) of 0.788. The AUCs of the five studied risk scores were 0.789, 0.793, 0.804, 0.810, and 0.826, respectively. The imaging biomarker's predictive performance was comparable to four of the risk scores but inferior to one (p = 0.038). Adding the imaging biomarker to the risk scores improved the AUCs (p-values) to 0.831 (0.003), 0.825 (0.001), 0.834 (0.003), 0.836 (0.003), and 0.839 (0.177), respectively. The net reclassification improvement and integrated discrimination improvement indices also showed significant improvements (all p < 0.001).

CONCLUSIONS

Using DL techniques to create an MR-based imaging biomarker is feasible and enhances the predictive ability of current risk scores.

Dopaminergic mesolimbic structural reserve is positively linked to better outcome after severe stroke

The concept of brain reserve capacity has emerged in stroke recovery research in recent years. Imaging-based biomarkers of brain health have helped to better understand outcome variability in clinical cohorts. Still, outcome inferences are far from being satisfactory, particularly in patients with severe initial deficits. Neurorehabilitation after stroke is a complex process, comprising adaption and learning processes, which, on their part, are critically influenced by motivational and reward-related cognitive processes. Amongst others, dopaminergic neurotransmission is a key contributor to these mechanisms. The question arises, whether the amount of structural reserve capacity in the dopaminergic system might inform about outcome variability after severe stroke. For this purpose, this study analysed imaging and clinical data of 42 severely impaired acute stroke patients. Brain volumetry was performed within the first 2 weeks after the event using the Computational Anatomy Toolbox CAT12, grey matter volume estimates were collected for seven key areas of the human dopaminergic system along the mesocortical, mesolimbic and nigrostriatal pathways. Ordinal logistic regression models related regional volumes to the functional outcome, operationalized by the modified Rankin Scale, obtained 3-6 months after stroke. Models were adjusted for age, lesion volume and initial impairment. The main finding was that larger volumes of the amygdala and the nucleus accumbens at baseline were positively associated with a more favourable outcome. These data suggest a link between the structural state of mesolimbic key areas contributing to motor learning, motivational and reward-related brain networks and potentially the success of neurorehabilitation. They might also provide novel evidence to reconsider dopaminergic interventions particularly in severely impaired stroke patients to enhance recovery after stroke.

Application of radiomics in ischemic stroke

In recent years, radiomics has emerged as a novel research methodology that plays a crucial role in the diagnosis and treatment of ischemic stroke. By integrating multimodal medical imaging techniques such as computed tomography and magnetic resonance imaging, radiomics offers in-depth insights into aspects such as the extent of brain tissue damage and hemodynamics. These data help physicians to accurately assess patient condition, select optimal treatment strategies, and predict recovery trajectories and long-term prognoses, thereby enhancing treatment efficacy and reducing the risk of complications. With the anticipated further advancements in radiomic technology, this methodology has great potential for expanded applications in the early detection, treatment, and prognosis of ischemic stroke. The present narrative review explores the burgeoning field of radiomics and its transformative impact on ischemic stroke.

Self-reporting with checklists in artificial intelligence research on medical imaging: a systematic review based on citations of CLAIM

OBJECTIVE

To evaluate the usage of a well-known and widely adopted checklist, Checklist for Artificial Intelligence in Medical imaging (CLAIM), for self-reporting through a systematic analysis of its citations.

METHODS

Google Scholar, Web of Science, and Scopus were used to search for citations (date, 29 April 2023). CLAIM's use for self-reporting with proof (i.e., filled-out checklist) and other potential use cases were systematically assessed in research papers. Eligible papers were evaluated independently by two readers, with the help of automatic annotation. Item-by-item confirmation analysis on papers with checklist proof was subsequently performed.

RESULTS

A total of 391 unique citations were identified from three databases. Of the 118 papers included in this study, 12 (10%) provided a proof of self-reported CLAIM checklist. More than half (70; 59%) only mentioned some sort of adherence to CLAIM without providing any proof in the form of a checklist. Approximately one-third (36; 31%) cited the CLAIM for reasons unrelated to their reporting or methodological adherence. Overall, the claims on 57 to 93% of the items per publication were confirmed in the item-by-item analysis, with a mean and standard deviation of 81% and 10%, respectively.

CONCLUSION

Only a small proportion of the publications used CLAIM as checklist and supplied filled-out documentation; however, the self-reported checklists may contain errors and should be approached cautiously. We hope that this systematic citation analysis would motivate artificial intelligence community about the importance of proper self-reporting, and encourage researchers, journals, editors, and reviewers to take action to ensure the proper usage of checklists.

CLINICAL RELEVANCE STATEMENT

Only a small percentage of the publications used CLAIM for self-reporting with proof (i.e., filled-out checklist). However, the filled-out checklist proofs may contain errors, e.g., false claims of adherence, and should be approached cautiously. These may indicate inappropriate usage of checklists and necessitate further action by authorities.

KEY POINTS

• Of 118 eligible papers, only 12 (10%) followed the CLAIM checklist for self-reporting with proof (i.e., filled-out checklist). More than half (70; 59%) only mentioned some kind of adherence without providing any proof. • Overall, claims on 57 to 93% of the items were valid in item-by-item confirmation analysis, with a mean and standard deviation of 81% and 10%, respectively. • Even with the checklist proof, the items declared may contain errors and should be approached cautiously.

Predicting long-term outcomes for acute ischemic stroke using multi-model MRI radiomics and clinical variables

Purpose

The objective of this study was to create and validate a novel prediction model that incorporated both multi-modal radiomics features and multi-clinical features, with the aim of accurately identifying acute ischemic stroke (AIS) patients who faced a higher risk of poor outcomes.

Methods

A cohort of 461 patients diagnosed with AIS from four centers was divided into a training cohort and a validation cohort. Radiomics features were extracted and selected from diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images to create a radiomic signature. Prediction models were developed using multi-clinical and selected radiomics features from DWI and ADC.

Results

A total of 49 radiomics features were selected from DWI and ADC images by the least absolute shrinkage and selection operator (LASSO). Additionally, 20 variables were collected as multi-clinical features. In terms of predicting poor outcomes in validation set, the area under the curve (AUC) was 0.727 for the DWI radiomics model, 0.821 for the ADC radiomics model, 0.825 for the DWI + ADC radiomics model, and 0.808 for the multi-clinical model. Furthermore, a prediction model was built using all selected features, the AUC for predicting poor outcomes increased to 0.86.

Conclusion

Radiomics features extracted from DWI and ADC images can serve as valuable biomarkers for predicting poor clinical outcomes in patients with AIS. Furthermore, when these radiomics features were combined with multi-clinical features, the predictive performance was enhanced. The prediction model has the potential to provide guidance for tailoring rehabilitation therapies based on individual patient risks for poor outcomes.

Mediation of Age and Thrombectomy Outcome by Neuroimaging Markers of Frailty in Patients With Stroke

Importance

Age is a leading predictor of poor outcomes after brain injuries like stroke. The extent to which age is associated with preexisting burdens of brain changes, visible on neuroimaging but rarely considered in acute decision-making or trials, is unknown.

Objectives

To explore the mediation of age on functional outcome by neuroimaging markers of frailty (hereinafter neuroimaging frailty) in patients with acute ischemic stroke receiving endovascular thrombectomy (EVT).

Design, Setting, and Participants

This cohort study was a post hoc analysis of the Safety and Efficacy of Nerinetide (NA-1) in Subjects Undergoing Endovascular Thrombectomy for Stroke (ESCAPE-NA1) randomized clinical trial, which investigated intravenous (IV) nerinetide in patients who underwent EVT within a 12-hour treatment window. Patients from 48 acute care hospitals in 8 countries (Canada, US, Germany, Korea, Australia, Ireland, UK, and Sweden) were enrolled between March 1, 2017, and August 12, 2019. Markers of brain frailty (brain atrophy [subcortical or cortical], white matter disease [periventricular or deep], and the number of lacunes and chronic infarctions) were retrospectively assessed while reviewers were blinded to other imaging (eg, computed tomography angiography, computed tomography perfusion) or outcome variables. All analyses were done between December 1, 2022, and January 31, 2023.

Exposures

All patients received EVT and were randomized to IV nerinetide (2.6 mg/kg of body weight) and alteplase (if indicated) treatment vs best medical management.

Main Outcome and Measures

The primary outcome was the proportion of the total effect of age on 90-day outcome, mediated by neuroimaging frailty. A combined mediation was also examined by clinical features associated with frailty and neuroimaging markers (total frailty). Structural equation modeling was used to create latent variables as potential mediators, adjusting for baseline, early ischemic changes; stroke severity; onset-to-puncture time; nerinetide treatment; and alteplase treatment.

Results

Among a total of 1105 patients enrolled in the study, 1102 (median age, 71 years [IQR, 61-80 years]; 554 [50.3%] male) had interpretable imaging at baseline. Of these participants, 549 (49.8%) were treated with IV nerinetide. The indirect effect of age on 90-day outcome, mediated by neuroimaging frailty, was associated with 85.1% of the total effect (β coefficient, 0.04 per year [95% CI, 0.02-0.06 per year]; P < .001). When including both frailty constructs, the indirect pathway was associated with essentially 100% of the total effect (β coefficient, 0.07 per year [95% CI, 0.03-0.10 per year]; P = .001).

Conclusions and Relevance

In this cohort study, a secondary analysis of the ESCAPE-NA1 trial, most of the association between age and 90-day outcome was mediated by neuroimaging frailty, underscoring the importance of features like brain atrophy and small vessel disease, as opposed to chronological age alone, in predicting poststroke outcomes. Future trials could include such frailty features to stratify randomization or improve adjustment in outcome analyses.

Designing machine learning for big data: A study to identify factors that increase the risk of ischemic stroke and prognosis in hypertensive patients

Background

Ischemic stroke (IS) accounts large amount of stroke incidence. The aim of this study was to discover the risk and prognostic factors that affecting the occurrence of IS in hypertensive patients.

Method

Study data were obtained from the Medical Information Mart for Intensive Care (MIMIC)-IV database. To avoid biased factors selection process, several approaches were studied including logistic regression, elastic net regression, random forest, correlation analysis, and multifactor logistic regression methods. And seven different machine-learning methods are used to construct predictive models. The performance of the developed models was evaluated using AUC (Area Under the Curve), prediction accuracy, precision, recall, F1 score, PPV (Positive Predictive Value) and NPV (Negative Predictive Value). Interaction analysis was conducted to explore potential relationships between influential factors.

Results

The study included 92,514 hypertensive patients, of which 1746 hypertensive patients experienced IS. The Gradient Boosted Decision Tree (GBDT) model outperformed the other prediction model terms of prediction accuracy and AUC values in both ischemic and prognosis cases. By using the SHapley Additive exPlanations (SHAP), we found that a range of factors and corresponding interactions between factors are important risk factors for IS and its prognosis in hypertensive patients.

Conclusion

The study identified factors that increase the risk of IS and poor prognosis in hypertensive patients, which may provide guidance for clinical diagnosis and treatment.

Post-Stroke Brain Health Monitoring and Optimization: A Narrative Review

Significant advancements have been made in recent years in the acute treatment and secondary prevention of stroke. However, a large proportion of stroke survivors will go on to have enduring physical, cognitive, and psychological disabilities from suboptimal post-stroke brain health. Impaired brain health following stroke thus warrants increased attention from clinicians and researchers alike. In this narrative review based on an open timeframe search of the PubMed, Scopus, and Web of Science databases, we define post-stroke brain health and appraise the body of research focused on modifiable vascular, lifestyle, and psychosocial factors for optimizing post-stroke brain health. In addition, we make clinical recommendations for the monitoring and management of post-stroke brain health at major post-stroke transition points centered on four key intertwined domains: cognition, psychosocial health, physical functioning, and global vascular health. Finally, we discuss potential future work in the field of post-stroke brain health, including the use of remote monitoring and interventions, neuromodulation, multi-morbidity interventions, enriched environments, and the need to address inequities in post-stroke brain health. As post-stroke brain health is a relatively new, rapidly evolving, and broad clinical and research field, this narrative review aims to identify and summarize the evidence base to help clinicians and researchers tailor their own approach to integrating post-stroke brain health into their practices.

MRI BrainAGE demonstrates increased brain aging in systemic lupus erythematosus patients

Introduction

Systemic lupus erythematosus (SLE) is an autoimmune connective tissue disease affecting multiple organs in the human body, including the central nervous system. Recently, an artificial intelligence method called BrainAGE (Brain Age Gap Estimation), defined as predicted age minus chronological age, has been developed to measure the deviation of brain aging from a healthy population using MRI. Our aim was to evaluate brain aging in SLE patients using a deep-learning BrainAGE model.

Methods

Seventy female patients with a clinical diagnosis of SLE and 24 healthy age-matched control females, were included in this post-hoc analysis of prospectively acquired data. All subjects had previously undergone a 3 T MRI acquisition, a neuropsychological evaluation and a measurement of neurofilament light protein in plasma (NfL). A BrainAGE model with a 3D convolutional neural network architecture, pre-trained on the 3D-T1 images of 1,295 healthy female subjects to predict their chronological age, was applied on the images of SLE patients and controls in order to compute the BrainAGE. SLE patients were divided into 2 groups according to the BrainAGE distribution (high vs. low BrainAGE).

Results

BrainAGE z-score was significantly higher in SLE patients than in controls (+0.6 [±1.1] vs. 0 [±1.0], p = 0.02). In SLE patients, high BrainAGE was associated with longer reaction times (p = 0.02), lower psychomotor speed (p = 0.001) and cognitive flexibility (p = 0.04), as well as with higher NfL after adjusting for age (p = 0.001).

Conclusion

Using a deep-learning BrainAGE model, we provide evidence of increased brain aging in SLE patients, which reflected neuronal damage and cognitive impairment.

Association of Brain Atrophy With Functional Outcome and Recovery Trajectories After Thrombectomy: Post Hoc Analysis of the ESCAPE-NA1 Trial

BACKGROUND AND OBJECTIVES

Brain frailty may impair the ability of acute stroke patients to cope with the injury, irrespective of their chronologic age, resulting in impaired recovery. We aim to investigate the impact of brain atrophy on functional outcome assessed at different time points after endovascular thrombectomy (EVT).

METHODS

In this retrospective post hoc analysis of the ESCAPE-NA1 trial, we analyzed CT imaging data for cortical atrophy by using the GCA scale, including region-specific scales, and subcortical atrophy by using the intercaudate distance to inner table width (CC/IT) ratio. The primary outcome was 90-day mRS (ordinal shift analysis), and the secondary outcome was the mRS score over time. Adjustments were made for age, sex, baseline NIHSS, final infarct volume, stroke laterality, total Fazekas score, and nerinetide-alteplase interaction. Sensitivity analyses were additionally performed in only those patients for whom MRI data were available.

RESULTS

Of 1,102 participants (mean age of 69.5 ± 13.7 years; 554 men), 818 (74%) had GCA = 0, 220 (20%) had GCA = 1, and 64 (6%) had GCA = 2/3. The median CC/IT ratio was 0.12 (IQR0.10-0.15). Cortical atrophy (GCA ≥ 1 vs GCA 0) was associated with worse 90-day mRS (acOR = 1.62 [95% CI 1.22-2.16]; p = 0.001), lower rates of 90-day mRS0-2 (aOR = 0.65 [95% CI 0.45-0.94]; p = 0.022), and higher mortality (aOR = 2.12 [95% CI 1.28-3.5]; p = 0.003), regardless of the region assessed. Subcortical atrophy was associated with worse 90-day mRS (acOR [per 0.01 increase in CC/IT ratio] = 1.07 [95% CI 1.04-1.11]; p < 0.001) and lower rates of 90-day mRS0-2 (aOR = 0.92 [95% CI 0.88-0.97]; p = 0.001). Furthermore, with various degrees of atrophy, we observed heterogeneity in mRS measurements during follow-up: worse mRS scores for higher atrophy grades (p < 0.001). Compared with participants with GCA = 0, the mRS for participants with GCA = 1 was higher at 30 days (adjusted difference = 0.41 [95% CI 0.18-0.65]) and remained worse at 90 days (adjusted difference = 0.72 [95% CI 0.49-0.95]). Similar effects were seen for participants with worse cortical atrophy, regardless of the region assessed, and worse subcortical atrophy. Furthermore, 26/63(41%) and 124/274(45%) patients with severe cortical/subcortical atrophy (GCA 2/3 and highest CC/IT ratio quartile, respectively) achieved good functional outcome (mRS0-2), compared with 539/812(66.4%) with no cortical atrophy and 209/274(76%) in the lowest CC/IT ratio quartile.

DISCUSSION

In this large RCT-derived population, participants with brain atrophy, as visually assessed on acute noncontrast computed tomography imaging, showed less favorable stroke recovery after EVT and worse 90-day functional outcomes compared with participants without brain atrophy. This may support physicians with recovery expectations when planning post-EVT care with patients and their families.

Cortical and Subcortical Brain Atrophy Assessment Using Simple Measures on NCCT Compared with MRI in Acute Stroke

BACKGROUND AND PURPOSE

Brain atrophy is an important surrogate for brain reserve, the capacity of the brain to cope with acquired injuries such as acute stroke. It is unclear how well atrophy measurements on MR imaging can be reproduced using NCCT imaging. We aimed to compare pragmatic atrophy measures on NCCT with MR imaging in patients with acute ischemic stroke.

MATERIALS AND METHODS

This is a post hoc analysis, including baseline NCCT and 24-hour follow-up MR imaging data from the Safety and Efficacy of Nerinetide (NA-1) in Subjects Undergoing Endovascular Thrombectomy for Stroke (ESCAPE-NA1) trial. Cortical atrophy was measured using the global cortical atrophy scale, and subcortical atrophy was measured using the intercaudate distance-to-inner-table width (CC/IT) ratio. Agreement and correlation between these measures on NCCT and MR imaging were calculated using the Gwet agreement coefficient 1 and Pearson correlation coefficients, respectively.

RESULTS

Among 1105 participants in the ESCAPE-NA1 trial, interpretable NCCT and 24-hour MR imaging were available in 558 (50.5%) patients (mean age, 67.2 [SD, 13.7] years; 282 women). Cortical atrophy assessments performed on NCCT underestimated atrophy severity compared with MR imaging (eg, patients with global cortical atrophy of ≥1 assessed on NCCT = 133/558 [23.8%] and on MR imaging = 247/558 [44.3%]; a 20.5% difference). Overall, cortical (ie, global cortical atrophy) atrophy assessments on NCCT had substantial or better agreement with MR imaging (Gwet agreement coefficient 1 of > 0.784; P < .001). Subcortical atrophy measures (CC/IT ratio) showed strong correlations between NCCT and MR imaging (Pearson correlation = 0.746, P < .001).

CONCLUSIONS

Brain atrophy can be evaluated using simple measures in emergently acquired NCCT. Subcortical atrophy assessments on NCCT show strong correlations with MR imaging. Although cortical atrophy assessments on NCCT are strongly correlated with MR imaging ratings, there is a general underestimation of atrophy severity on NCCT.