Orbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots

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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Abstract WMP23: Automated ASPECTS Scoring of CT Scans for Acute Ischemic Stroke Patients Using Machine Learning

Stroke ◽

10.1161/str.49.suppl_1.wmp23 ◽

2018 ◽

Vol 49 (Suppl_1) ◽

Author(s):

Hulin Kuang ◽

Ericka Teleg ◽

Mohamed Najm ◽

Alexis T Wilson ◽

Sung I Sohn ◽

...

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Ct Scans ◽

Stroke Patients

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Platelet-rich clots as identified by Martius Scarlet Blue staining are isodense on NCCT

Journal of NeuroInterventional Surgery ◽

10.1136/neurintsurg-2018-014637 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1145-1149 ◽

Cited By ~ 9

Author(s):

Sean T Fitzgerald ◽

Shunli Wang ◽

Daying Dai ◽

Andrew Douglas ◽

Ramanathan Kadirvel ◽

...

Keyword(s):

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Inverse Correlation ◽

Clinical Information ◽

Blue Staining ◽

Histopathological Analysis ◽

Blue Stain ◽

The Mean ◽

Learning Software ◽

Ct Density

BackgroundCurrent studies on clot characterization in acute ischemic stroke focus on fibrin and red blood cell composition. Few studies have examined platelet composition in acute ischemic stroke clots. We characterize clot composition using the Martius Scarlet Blue stain and assess associations between platelet density and CT density.Materials and methodHistopathological analysis of the clots collected as part of the multi-institutional STRIP registry was performed using Martius Scarlet Blue stain and the composition of the clots was quantified using Orbit Image Analysis (www.orbit.bio) machine learning software. Prior to endovascular treatment, each patient underwent non-contrast CT (NCCT) and the CT density of each clot was measured. Correlations between clot components and clinical information were assessed using the χ2 test.ResultsEighty-five patients were included in the study. The mean platelet density of the clots was 15.7% (2.5–72.5%). There was a significant correlation between platelet-rich clots and the absence of hyperdensity on NCCT, (ρ=0.321, p=0.003*, n=85). Similarly, there was a significant inverse correlation between the percentage of platelets and the mean Hounsfield Units on NCCT (ρ=−0.243, p=0.025*, n=85).ConclusionMartius Scarlet Blue stain can identify patients who have platelet-rich clots. Platelet-rich clots are isodense on NCCT.

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Machine learning is a valid method for predicting prehospital delay after acute ischemic stroke

Brain and Behavior ◽

10.1002/brb3.1794 ◽

2020 ◽

Vol 10 (10) ◽

Author(s):

Li Yang ◽

Qinqin Liu ◽

Qiuli Zhao ◽

Xuemei Zhu ◽

Ling Wang

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Prehospital Delay ◽

Valid Method

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Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea

Diagnostics ◽

10.3390/diagnostics11101909 ◽

2021 ◽

Vol 11 (10) ◽

pp. 1909

Author(s):

Dougho Park ◽

Eunhwan Jeong ◽

Haejong Kim ◽

Hae Wook Pyun ◽

Haemin Kim ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Functional Outcome ◽

Outcome Prediction ◽

Prediction Models ◽

Gradient Boosting ◽

Support Vector ◽

Extreme Gradient Boosting

Background: Functional outcomes after acute ischemic stroke are of great concern to patients and their families, as well as physicians and surgeons who make the clinical decisions. We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. Methods: This retrospective study used a prospective cohort database. A total of 1066 patients with acute ischemic stroke between January 2019 and March 2021 were included. Variables such as demographic factors, stroke-related factors, laboratory findings, and comorbidities were utilized at the time of admission. Five ML algorithms were applied to predict a favorable functional outcome (modified Rankin Scale 0 or 1) at 3 months after stroke onset. Results: Regularized logistic regression showed the best performance with an area under the receiver operating characteristic curve (AUC) of 0.86. Support vector machines represented the second-highest AUC of 0.85 with the highest F1-score of 0.86, and finally, all ML models applied achieved an AUC > 0.8. The National Institute of Health Stroke Scale at admission and age were consistently the top two important variables for generalized logistic regression, random forest, and extreme gradient boosting models. Conclusions: ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful.

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Treatment Efficacy Analysis in Acute Ischemic Stroke Patients Using In Silico Modeling Based on Machine Learning: A Proof-of-Principle

Biomedicines ◽

10.3390/biomedicines9101357 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1357

Author(s):

Anthony Winder ◽

Matthias Wilms ◽

Jens Fiehler ◽

Nils D. Forkert

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Treatment Group ◽

Acute Ischemic Stroke ◽

In Silico ◽

Mechanical Thrombectomy ◽

Intravenous Thrombolysis ◽

Stroke Patients ◽

New Devices

Interventional neuroradiology is characterized by engineering- and experience-driven device development with design improvements every few months. However, clinical validation of these new devices requires lengthy and expensive randomized controlled trials. This contribution proposes a machine learning-based in silico study design to evaluate new devices more quickly with a small sample size. Acute diffusion- and perfusion-weighted MRI, segmented one-week follow-up imaging, and clinical variables were available for 90 acute ischemic stroke patients. Three treatment option-specific random forest models were trained to predict the one-week follow-up lesion segmentation for (1) patients successfully recanalized using intra-arterial mechanical thrombectomy, (2) patients successfully recanalized using intravenous thrombolysis, and (3) non-recanalizing patients as an analogue for conservative treatment for each patient in the sample, independent of the true group membership. A repeated-measures analysis of the three predicted follow-up lesions for each patient revealed significantly larger lesions for the non-recanalizing group compared to the successful intravenous thrombolysis treatment group, which in turn showed significantly larger lesions compared to the successful mechanical thrombectomy treatment group (p < 0.001). A groupwise comparison of the true follow-up lesions for the three treatment options showed the same trend but did not reach statistical significance (p = 0.19). We conclude that the proposed machine learning-based in silico trial design leads to clinically feasible results and can support new efficacy studies by providing additional power and potential early intermediate results.

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Predicting 1-Hour Thrombolysis Effect of r-tPA in Patients With Acute Ischemic Stroke Using Machine Learning Algorithm

Frontiers in Pharmacology ◽

10.3389/fphar.2021.759782 ◽

2022 ◽

Vol 12 ◽

Author(s):

Bin Zhu ◽

Jianlei Zhao ◽

Mingnan Cao ◽

Wanliang Du ◽

Liuqing Yang ◽

...

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Learning Algorithm ◽

Degradation Products ◽

Early Stage ◽

Area Under The Curve ◽

Recursive Feature Elimination ◽

Gradient Boosting ◽

Data Sets

Background: Thrombolysis with r-tPA is recommended for patients after acute ischemic stroke (AIS) within 4.5 h of symptom onset. However, only a few patients benefit from this therapeutic regimen. Thus, we aimed to develop an interpretable machine learning (ML)–based model to predict the thrombolysis effect of r-tPA at the super-early stage.Methods: A total of 353 patients with AIS were divided into training and test data sets. We then used six ML algorithms and a recursive feature elimination (RFE) method to explore the relationship among the clinical variables along with the NIH stroke scale score 1 h after thrombolysis treatment. Shapley additive explanations and local interpretable model–agnostic explanation algorithms were applied to interpret the ML models and determine the importance of the selected features.Results: Altogether, 353 patients with an average age of 63.0 (56.0–71.0) years were enrolled in the study. Of these patients, 156 showed a favorable thrombolysis effect and 197 showed an unfavorable effect. A total of 14 variables were enrolled in the modeling, and 6 ML algorithms were used to predict the thrombolysis effect. After RFE screening, seven variables under the gradient boosting decision tree (GBDT) model (area under the curve = 0.81, specificity = 0.61, sensitivity = 0.9, and F1 score = 0.79) demonstrated the best performance. Of the seven variables, activated partial thromboplastin clotting time (time), B-type natriuretic peptide, and fibrin degradation products were the three most important clinical characteristics that might influence r-tPA efficiency.Conclusion: This study demonstrated that the GBDT model with the seven variables could better predict the early thrombolysis effect of r-tPA.

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Automated prediction of final infarct volume in patients with large-vessel occlusion acute ischemic stroke

Neurosurgical FOCUS ◽

10.3171/2021.4.focus21134 ◽

2021 ◽

Vol 51 (1) ◽

pp. E13

Author(s):

Rania Abdelkhaleq ◽

Youngran Kim ◽

Swapnil Khose ◽

Peter Kan ◽

Sergio Salazar-Marioni ◽

...

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Infarct Volume ◽

Large Vessel Occlusion ◽

Infarct Core ◽

Vessel Occlusion ◽

Automated Method ◽

Machine Learning Model ◽

Time Of Presentation

OBJECTIVE In patients with large-vessel occlusion (LVO) acute ischemic stroke (AIS), determinations of infarct size play a key role in the identification of candidates for endovascular stroke therapy (EVT). An accurate, automated method to quantify infarct at the time of presentation using widely available imaging modalities would improve screening for EVT. Here, the authors aimed to compare the performance of three measures of infarct core at presentation, including an automated method using machine learning. METHODS Patients with LVO AIS who underwent successful EVT at four comprehensive stroke centers were identified. Patients were included if they underwent concurrent noncontrast head CT (NCHCT), CT angiography (CTA), and CT perfusion (CTP) with Rapid imaging at the time of presentation, and MRI 24 to 48 hours after reperfusion. NCHCT scans were analyzed using the Alberta Stroke Program Early CT Score (ASPECTS) graded by neuroradiology or neurology expert readers. CTA source images were analyzed using a previously described machine learning model named DeepSymNet (DSN). Final infarct volume (FIV) was determined from diffusion-weighted MRI sequences using manual segmentation. The primary outcome was the performance of the three infarct core measurements (NCHCT-ASPECTS, CTA with DSN, and CTP-Rapid) to predict FIV, which was measured using area under the receiver operating characteristic (ROC) curve (AUC) analysis. RESULTS Among 76 patients with LVO AIS who underwent EVT and met inclusion criteria, the median age was 67 years (IQR 54–76 years), 45% were female, and 37% were White. The median National Institutes of Health Stroke Scale score was 16 (IQR 12–22), and the median NCHCT-ASPECTS on presentation was 8 (IQR 7–8). The median time between when the patient was last known to be well and arrival was 156 minutes (IQR 73–303 minutes), and between NCHCT/CTA/CTP to groin puncture was 73 minutes (IQR 54–81 minutes). The AUC was obtained at three different cutoff points: 10 ml, 30 ml, and 50 ml FIV. At the 50-ml FIV cutoff, the AUC of ASPECTS was 0.74; of CTP core volume, 0.72; and of DSN, 0.82. Differences in AUCs for the three predictors were not significant for the three FIV cutoffs. CONCLUSIONS In a cohort of patients with LVO AIS in whom reperfusion was achieved, determinations of infarct core at presentation by NCHCT-ASPECTS and a machine learning model analyzing CTA source images were equivalent to CTP in predicting FIV. These findings have suggested that the information to accurately predict infarct core in patients with LVO AIS was present in conventional imaging modalities (NCHCT and CTA) and accessible by machine learning methods.

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