Automated ASPECTS on Noncontrast CT Scans in Patients with Acute Ischemic Stroke Using Machine Learning

Introduction: Intra-arterial therapy for acute ischemic stroke (AIS) now has an established role. We investigated if Hounsfield Units (HU) quantification on noncontrast CT is associated with ease and efficacy of mechanical thrombectomy and outcomes. Methods: We retrospectively studied a prospectively maintained database of cases of acute ischemic stroke that underwent intra-arterial therapy between May 2008 and August 2012. Functional outcome was assessed by ninety-day follow up modified Rankin Scale (mRS). Patients were dichotomized base on time to recanalization. Hounsfield units were calculated on head CT. Thrombus location and length were determined on CT angiography. Simple linear regression was used to analyze the association between clot length, average HU, and other clinical variables. Results: 141 patients were included. There was no difference in clot length or average HU among patients with good recanalization achieved within an hour compared to those in which procedures extended beyond an hour. There was no relationship between clot length or density and recanalization. The thrombus length and density were not significantly different between patients with procedural complications and those without. The presence of post procedure intracranial hemorrhage was not associated with thrombus length or density. Ninety day mRS was not associated with thrombus length or density. Conclusions: We have not found any significant associations between either thrombus length or density and likelihood of recanalization, time to achieve recanalization, intraprocedural complications, postprocedural hemorrhage or functional outcome at ninety days. These results do not support a predictive value for thrombus quantification in the evaluation of AIS.

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A Detailed Analysis of Infarct Patterns and Volumes at 24-hour Noncontrast CT and Diffusion-weighted MRI in Acute Ischemic Stroke Due to Large Vessel Occlusion: Results from the ESCAPE-NA1 Trial

Radiology ◽

10.1148/radiol.2021203964 ◽

2021 ◽

pp. 203964

Author(s):

Johanna M. Ospel ◽

Bijoy K. Menon ◽

Wu Qiu ◽

Nima Kashani ◽

Arnuv Mayank ◽

...

Keyword(s):

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Detailed Analysis ◽

Large Vessel ◽

Diffusion Weighted Mri ◽

Large Vessel Occlusion ◽

Vessel Occlusion ◽

Diffusion Weighted ◽

Noncontrast Ct ◽

And Diffusion

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Abstract TP215: Machine Learning Analysis of Gene Expression Data and Predictive Stroke Outcomes in Acute Ischemic Stroke

Stroke ◽

10.1161/str.51.suppl_1.tp215 ◽

2020 ◽

Vol 51 (Suppl_1) ◽

Author(s):

Sarah R Martha ◽

Qiang Cheng ◽

Liyu Gong ◽

Lisa Collier ◽

Stephanie Davis ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Mechanical Thrombectomy ◽

Stroke Patients ◽

Stroke Outcomes ◽

Inflammatory Genes ◽

Patient Demographics ◽

Edema Volume

Background and Purpose: The ability to predict ischemic stroke outcomes in the first day of admission could be vital for patient counseling, rehabilitation, and care planning. The Blood and Clot Thrombectomy Registry and Collaboration (BACTRAC; clinicaltrials.gov NCT03153683) collects blood samples distal and proximal to the intracranial thrombus during mechanical thrombectomy. These samples are a novel resource in evaluating acute gene expression changes at the time of ischemic stroke. The purpose of this study was to identify inflammatory genes and patient demographics that are predictive of stroke outcomes (infarct and/or edema volume) in acute ischemic stroke patients. Methods: The BACTRAC study is a non-probability, convenience sampling of subjects (≥ 18 year olds) treated with mechanical thrombectomy for emergent large vessel occlusion. We evaluated relative concentrations of mRNA for gene expression in 84 inflammatory molecules in static blood distal and proximal to the intracranial thrombus from adults who underwent thrombectomy. We employed a machine learning method, Random Forest, utilizing the first set of enrolled subjects, to predict which inflammatory genes and patient demographics were important features for infarct and edema volumes. Results: We analyzed the first 28 subjects (age = 66 ± 15.48, 11 males) in the BACTRAC registry. Results from machine learning analyses demonstrate that the genes CCR4, IFNA2, IL9, CXCL3, Age, DM, IL7, CCL4, BMI, IL5, CCR3, TNF, and IL27 predict infarct volume. The genes IFNA2, IL5, CCL11, IL17C, CCR4, IL9, IL7, CCR3, IL27, DM, and CSF2 predict edema volume. There is an intersection of genes CCR4, IFNA2, IL9, IL7, IL5, CCR3 to both infarct and edema volumes. Overall, these genes depicts a microenvironment for chemoattraction and proliferation of autoimmune cells, particularly Th2 cells and neutrophils. Conclusions: Machine learning algorithms can be employed to develop predictive biomarker signatures for stroke outcomes in ischemic stroke patients, particularly in regard to identifying acute gene expression changes that occur during stroke.

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