Application of machine learning to automated analysis of cerebral edema in large cohorts of ischemic stroke patients

Cerebral edema contributes to neurological deterioration and death after hemispheric stroke but there remains no effective means of preventing or accurately predicting its occurrence. Big data approaches may provide insights into the biologic variability and genetic contributions to severity and time course of cerebral edema. These methods require quantitative analyses of edema severity across large cohorts of stroke patients. We have proposed that changes in cerebrospinal fluid (CSF) volume over time may represent a sensitive and dynamic marker of edema progression that can be measured from routinely available CT scans. To facilitate and scale up such approaches we have created a machine learning algorithm capable of segmenting and measuring CSF volume from serial CT scans of stroke patients. We now present results of our preliminary processing pipeline that was able to efficiently extract CSF volumetrics from an initial cohort of 155 subjects enrolled in a prospective longitudinal stroke study. We demonstrate a high degree of reproducibility in total cranial volume registration between scans (R = 0.982) as well as a strong correlation of baseline CSF volume and patient age (as a surrogate of brain atrophy, R = 0.725). Reduction in CSF volume from baseline to final CT was correlated with infarct volume (R = 0.715) and degree of midline shift (quadratic model, p < 2.2 × 10−16). We utilized generalized estimating equations (GEE) to model CSF volumes over time (using linear and quadratic terms), adjusting for age. This model demonstrated that CSF volume decreases over time (p < 2.2 × 10−13) and is lower in those with cerebral edema (p = 0.0004). We are now fully automating this pipeline to allow rapid analysis of even larger cohorts of stroke patients from multiple sites using an XNAT (eXtensible Neuroimaging Archive Toolkit) platform. Data on kinetics of edema across thousands of patients will facilitate precision approaches to prediction of malignant edema as well as modeling of variability and further understanding of genetic variants that influence edema severity

RP11-362K2.2:RP11-767I20.1 genetic variation is associated with post-reperfusion therapy parenchymal hematoma. A GWAS meta-analysis

Stroke is one of the most common causes of death and disability. Reperfusion therapies are the only treatment available during the acute phase of stroke. Due to recent clinical trials, these therapies may increase their frequency of use by extending the time-window administration, which may lead to an increase in complications such as hemorrhagic transformation, with parenchymal hematoma (PH) being the more severe subtype, associated with higher mortality and disability rates. Our aim was to find genetic risk factors associated with PH, as that could provide molecular targets/pathways for their prevention/treatment and study its genetic correlations to find traits sharing genetic background. We performed a GWAS and meta-analysis, following standard quality controls and association analysis (fastGWAS), adjusting age, NIHSS, and principal components. FUMA was used to annotate, prioritize, visualize, and interpret the meta-analysis results. The total number of patients in the meta-analysis was 2034 (216 cases and 1818 controls). We found rs79770152 having a genome-wide significant association (beta 0.09

A polygenic risk score based on a cardioembolic stroke multitrait analysis improves a clinical prediction model for this stroke subtype

Background: Occult atrial fibrillation (AF) is one of the major causes of embolic stroke of undetermined source (ESUS). Knowing the underlying etiology of an ESUS will reduce stroke recurrence and/or unnecessary use of anticoagulants. Understanding cardioembolic strokes (CES), whose main cause is AF, will provide tools to select patients who would benefit from anticoagulants among those with ESUS or AF. We aimed to discover novel loci associated with CES and create a polygenetic risk score (PRS) for a more efficient CES risk stratification. Methods: Multitrait analysis of GWAS (MTAG) was performed with MEGASTROKE-CES cohort ( Results: We found and replicated eleven loci associated with CES. Eight were novel loci. Seven of them had been previously associated with AF, namely, Conclusion: The loci found significantly associated with CES in the MTAG, together with the creation of a PRS that improves the predictive clinical models of CES, might help guide future clinical trials of anticoagulant therapy in patients with ESUS or AF

Rate of infarct-edema growth on CT predicts need for surgical intervention and clinical outcome in patients with cerebellar infarction

BACKGROUND: Up to 20% of patients with cerebellar infarcts will develop malignant edema and deteriorate clinically. Radiologic measures, such as initial infarct size, aid in identifying individuals at risk. Studies of anterior circulation stroke suggest that mapping early edema formation improves the ability to predict deterioration; however, the kinetics of edema in the posterior fossa have not been well characterized. We hypothesized that faster edema growth within the first hours after acute cerebellar stroke would be an indicator for individuals requiring surgical intervention and those with worse neurological outcomes. METHODS: Consecutive patients admitted to the neurological intensive care unit with acute cerebellar infarction were retrospectively identified. Hypodense regions of infarct and associated edema, infarct-edema , were delineated by using ABC/2 for all computed tomography (CT) scans up to 14 days from last known well. To examine how rate of infarct-edema growth varied across clinical variables and surgical intervention status, nonlinear and linear mixed-effect models were performed over 2 weeks and 2 days, respectively. In patients with at least two CT scans, multivariable logistic regression examined clinical and radiological predictors of surgical intervention (defined as extraventricular drainage and/or posterior fossa decompression) and poor clinical outcome (discharge to skilled nursing facility, long-term acute care facility, hospice, or morgue). RESULTS: Of 150 patients with acute cerebellar infarction, 38 (25%) received surgical intervention and 45 (30%) had poor clinical outcome. Age, admission National Institutes of Health Stroke Scale (NIHSS) score, and baseline infarct-edema volume did not differ, but bilateral/multiple vascular territory involvement was more frequent (87% vs. 50%, p \u3c 0.001) in the surgical group than that in the medical intervention group. On 410 serial CTs, infarct-edema volume progressed rapidly over the first 2 days, followed by a subsequent plateau. Of 112 patients who presented within two days, infarct-edema growth rate was greater in the surgical group (20.1 ml/day vs. 8.01 ml/day, p = 0.002). Of 67 patients with at least two scans, after adjusting for baseline infarct-edema volume, vascular territory, and NIHSS, infarct-edema growth rate over the first 2 days (odds ratio 2.55; 95% confidence interval 1.40-4.65) was an independent, and the strongest, predictor of surgical intervention. Further, early infarct-edema growth rate predicted poor clinical outcome (odds ratio 2.20; 95% confidence interval 1.30-3.71), independent of baseline infarct-edema volume, brainstem infarct, and NIHSS. CONCLUSIONS: Early infarct-edema growth rate, measured via ABC/2, is a promising biomarker for identifying the need for surgical intervention in patients with acute cerebellar infarction. Additionally, it may be used to facilitate discussions regarding patient prognosis

The Stroke Neuro-Imaging Phenotype Repository: An open data science platform for stroke research

Stroke is one of the leading causes of death and disability worldwide. Reducing this disease burden through drug discovery and evaluation of stroke patient outcomes requires broader characterization of stroke pathophysiology, yet the underlying biologic and genetic factors contributing to outcomes are largely unknown. Remedying this critical knowledge gap requires deeper phenotyping, including large-scale integration of demographic, clinical, genomic, and imaging features. Such big data approaches will be facilitated by developing and running processing pipelines to extract stroke-related phenotypes at large scale. Millions of stroke patients undergo routine brain imaging each year, capturing a rich set of data on stroke-related injury and outcomes. The Stroke Neuroimaging Phenotype Repository (SNIPR) was developed as a multi-center centralized imaging repository of clinical computed tomography (CT) and magnetic resonance imaging (MRI) scans from stroke patients worldwide, based on the open source XNAT imaging informatics platform. The aims of this repository are to: (i) store, manage, process, and facilitate sharing of high-value stroke imaging data sets, (ii) implement containerized automated computational methods to extract image characteristics and disease-specific features from contributed images, (iii) facilitate integration of imaging, genomic, and clinical data to perform large-scale analysis of complications after stroke; and (iv) develop SNIPR as a collaborative platform aimed at both data scientists and clinical investigators. Currently, SNIPR hosts research projects encompassing ischemic and hemorrhagic stroke, with data from 2,246 subjects, and 6,149 imaging sessions from Washington University\u27s clinical image archive as well as contributions from collaborators in different countries, including Finland, Poland, and Spain. Moreover, we have extended the XNAT data model to include relevant clinical features, including subject demographics, stroke severity (NIH Stroke Scale), stroke subtype (using TOAST classification), and outcome [modified Rankin Scale (mRS)]. Image processing pipelines are deployed on SNIPR using containerized modules, which facilitate replicability at a large scale. The first such pipeline identifies axial brain CT scans from DICOM header data and image data using a meta deep learning scan classifier, registers serial scans to an atlas, segments tissue compartments, and calculates CSF volume. The resulting volume can be used to quantify the progression of cerebral edema after ischemic stroke. SNIPR thus enables the development and validation of pipelines to automatically extract imaging phenotypes and couple them with clinical data with the overarching aim of enabling a broad understanding of stroke progression and outcomes

CSF Volumetric Analysis for Quantification of Cerebral Edema After Hemispheric Infarction

Malignant cerebral edema (CED) complicates at least 20 % of large hemispheric infarcts (LHI) and may result in neurological deterioration or death. Midline shift (MLS) is a standard but crude measure of edema severity. We propose that volumetric analysis of shifts in cerebrospinal fluid (CSF) over time provides a reliable means of quantifying the spectrum of edema severity after LHI

Effect of High-Dose Simvastatin on Cerebral Blood Flow and Static Autoregulation in Subarachnoid Hemorrhage

Statins may promote vasodilation following subarachnoid hemorrhage (SAH) and improve the response to blood pressure elevation. We sought to determine whether simvastatin increases cerebral blood flow (CBF) and alters the response to induced hypertension after SAH

Automated measurement of net water uptake from baseline and follow-up CTs in patients with large vessel occlusion stroke

Quantifying the extent and evolution of cerebral edema developing after stroke is an important but challenging goal. Lesional net water uptake (NWU) is a promising CT-based biomarker of edema, but its measurement requires manually delineating infarcted tissue and mirrored regions in the contralateral hemisphere. We implement an imaging pipeline capable of automatically segmenting the infarct region and calculating NWU from both baseline and follow-up CTs of large-vessel occlusion (LVO) patients. Infarct core is extracted from CT perfusion images using a deconvolution algorithm while infarcts on follow-up CTs were segmented from non-contrast CT (NCCT) using a deep-learning algorithm. These infarct masks were flipped along the brain midline to generate mirrored regions in the contralateral hemisphere of NCCT; NWU was calculated as one minus the ratio of densities between regions, removing voxels segmented as CSF and with HU outside thresholds of 20-80 (normal hemisphere and baseline CT) and 0-40 (infarct region on follow-up). Automated results were compared with those obtained using manually-drawn infarcts and an ASPECTS region-of-interest based method that samples densities within the infarct and normal hemisphere, using intraclass correlation coefficient (ρ). This was tested on serial CTs from 55 patients with anterior circulation LVO (including 66 follow-up CTs). Baseline NWU using automated core was 4.3% (IQR 2.6-7.3) and correlated with manual measurement (ρ = 0.80