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

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

American Journal of Neuroradiology ◽

10.3174/ajnr.a5889 ◽

2018 ◽

Vol 40 (1) ◽

pp. 33-38 ◽

Cited By ~ 27

Author(s):

H. Kuang ◽

M. Najm ◽

D. Chakraborty ◽

N. Maraj ◽

S.I. Sohn ◽

...

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Ct Scans ◽

Noncontrast Ct

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Using multiclass machine learning model to improve outcome prediction of acute ischemic stroke patients after reperfusion therapy

2020 International Computer Symposium (ICS) ◽

10.1109/ics51289.2020.00053 ◽

2020 ◽

Author(s):

I-Min Chiu ◽

Wun-Huei Zeng ◽

Chun-Hung Richard Lin

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Outcome Prediction ◽

Reperfusion Therapy ◽

Learning Model ◽

Stroke Patients ◽

Improve Outcome ◽

Machine Learning Model

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Predicting Clinical Outcomes in Acute Ischemic Stroke Patients Undergoing Endovascular Thrombectomy with Machine Learning

Clinical Neuroradiology ◽

10.1007/s00062-020-00990-3 ◽

2021 ◽

Author(s):

Yao Hao Teo ◽

Isis Claire Z. Y. Lim ◽

Fan Shuen Tseng ◽

Yao Neng Teo ◽

Cheryl Shumin Kow ◽

...

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Clinical Outcomes ◽

Stroke Patients ◽

Endovascular Thrombectomy

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Using a Multiclass Machine Learning Model to Predict the Outcome of Acute Ischemic Stroke Requiring Reperfusion Therapy

Diagnostics ◽

10.3390/diagnostics11010080 ◽

2021 ◽

Vol 11 (1) ◽

pp. 80

Author(s):

I-Min Chiu ◽

Wun-Huei Zeng ◽

Chi-Yung Cheng ◽

Shih-Hsuan Chen ◽

Chun-Hung Richard Lin

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Outcome Prediction ◽

Reperfusion Therapy ◽

Gradient Boosting ◽

Stroke Patients ◽

Extreme Gradient Boosting ◽

Machine Learning Models

Prediction of functional outcome in ischemic stroke patients is useful for clinical decisions. Previous studies mostly elaborate on the prediction of favorable outcomes. Miserable outcomes, which are usually defined as modified Rankin Scale (mRS) 5–6, should be considered as well before further invasive intervention. By using a machine learning algorithm, we aimed to develop a multiclass classification model for outcome prediction in acute ischemic stroke patients requiring reperfusion therapy. This was a retrospective study performed at a stroke medical center in Taiwan. Patients with acute ischemic stroke who visited between January 2016 and December 2019 and who were candidates for reperfusion therapy were included. Clinical outcomes were classified as favorable outcome, intermediate outcome, and miserable outcome. We developed four different multiclass machine learning models (Logistic Regression, Supportive Vector Machine, Random Forest, and Extreme Gradient Boosting) to predict clinical outcomes and compared their performance to the DRAGON score. A sample of 590 patients was included in this study. Of them, 180 (30.5%) had favorable outcomes and 152 (25.8%) had miserable outcomes. All selected machine learning models outperformed the DRAGON score on accuracy of outcome prediction (Logistic Regression: 0.70, Supportive Vector Machine: 0.67, Random Forest: 0.69, and Extreme Gradient Boosting: 0.67, vs. DRAGON: 0.51, p < 0.001). Among all selected models, Logistic Regression also had a better performance than the DRAGON score on positive predictive value, sensitivity, and specificity. Compared with the DRAGON score, the multiclass machine learning approach showed better performance on the prediction of the 3-month functional outcome of acute ischemic stroke patients requiring reperfusion therapy.

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Abstract WP368: Evaluation of Brain Regional Characteristics Related With Post-Stroke Delirium Using Machine Learning and Statistical Analysis

Stroke ◽

10.1161/str.51.suppl_1.wp368 ◽

2020 ◽

Vol 51 (Suppl_1) ◽

Author(s):

Seo Hyun Lee ◽

Yun Kyong Hyon ◽

Hee Won Yang ◽

Ki Wan Jeon ◽

...

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Statistical Analysis ◽

Acute Ischemic Stroke ◽

Cerebral Hemisphere ◽

Support Vector ◽

Stroke Patients ◽

Regional Characteristics ◽

Post Stroke ◽

The Brain

Background: The present study was performed to evaluate the brain regional characteristics related with development of post-stroke delirium using the machine learning and statistical analysis. Method: We used clinical and radiological data prospectively collected from 675 acute ischemic stroke patients, who were admitted in stroke unit from August 2017 to July 2018. Delirium occurrence in the patients was screened with Confusion Assessment Method and finally diagnosed using the criteria of the Diagnostic and Statistical Manual of Mental Disorders (fifth edition). Three machine learning models, Support Vector Machine (SVM), Random Forest (RF) and Tree-based Gradient Boosting (XGBoost), were applied for the prediction of post-stroke delirium with the clinical and radiologic data. And logistic regression analysis was performed to evaluate the significance of the brain regional parameters included in the importance features which were obtained from the XGBoost result. Results: Post-stroke delirium occurred in 66 (9.8%) of the total patients. On the comparison of the test accuracy to predict delirium occurrence, RF (94%), XGBoost test (93%), and SVM (89%) showed similar prediction rates. Of the brain regional parameters included in the top 30 feature importance, right side cerebral hemisphere, non-lacunar infarction, severity of periventricular white matter changes, acute temporal lobe lesion, cerebellum, brain stem, and previous lesions developed on right side cerebral hemisphere, and in temporal or frontal lobe. Conclusion: The present study shows that the brain regional characteristics related with the post-stroke delirium are shown to be significant when controlling the other features using statistical analysis with machine learning. Even though we need more studies to validate the relationships between post-stroke delirium and brain regional characteristics, the present brain regional characteristics could provide significant evidences to predict post-stroke delirium for the acute ischemic stroke patients.

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Abstract P506: Machine Learning Models Improve Prediction of Large Vessel Occlusion and Mechanical Thrombectomy Candidacy in Acute Ischemic Stroke

Stroke ◽

10.1161/str.52.suppl_1.p506 ◽

2021 ◽

Vol 52 (Suppl_1) ◽

Author(s):

Shon Thomas ◽

Paula De La Pena ◽

Liam Butler ◽

Oguz Akbilgic ◽

Daniel Heiferman ◽

...

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Random Forest ◽

Acute Ischemic Stroke ◽

Predictive Value ◽

Mechanical Thrombectomy ◽

Demographic Variables ◽

Large Vessel ◽

Stroke Patients ◽

Forest Model

Background and Purpose: Early identification of large vessel occlusions (LVO) and timely recanalization are paramount in improved clinical outcomes in acute ischemic stroke. Multiple simple stroke scales have good sensitivity, but compromise specificity for predicting LVO. No scale has been shown to predict mechanical thrombectomy (MT) candidacy. Machine learning techniques are being used for predictive modeling in many aspects of stroke care and may have potential in predicting LVO presence and MT candidacy. Methods: 287 acute ischemic stroke patients from July 2018 to July 2019 at Loyola University Medical Center were included. 36 clinical and demographic variables were analyzed using machine learning and statistical algorithms, including logistic regression, extreme gradient boosting, random forest, and decision trees to build models predictive of LVO and MT. The best performing model was compared with prior stroke scales. Results: Random forest based model resulted in the highest classification performance to predict both LVO and MT outcomes with an area under the curve (AUC) of 0.90±0.07 and 0.94±0.04, respectively. When the predictors were reduced to 7, random forest maintained a high AUC for predicting LVO (0.89). When reduced to 10 predictors, the random forest model predicted MT with an AUC = 0.93. Random forest models had excellent sensitivity and specificity of 0.86 and 0.89 for LVO and 0.89 and 0.95 for MT, respectively. The negative predictive value was 0.94 for LVO and 0.98 for MT while the positive predictive value was 0.77 for LVO and 0.79 for MT. With equal sensitivity, the random forest model was favorable to all previous stroke scales. Conclusion: Machine learning utilizing clinical and demographic variables predicts LVO and patient candidacy for MT with a high degree of accuracy. Further validation of this strategy for triage of stroke patients requires prospective and external validation.

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Localized prediction of tissue outcome in acute ischemic stroke patients using diffusion- and perfusion-weighted MRI datasets

PLoS ONE ◽

10.1371/journal.pone.0241917 ◽

2020 ◽

Vol 15 (11) ◽

pp. e0241917

Author(s):

Malte Grosser ◽

Susanne Gellißen ◽

Patrick Borchert ◽

Jan Sedlacik ◽

Jawed Nawabi ◽

...

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Outcome Prediction ◽

Hybrid Approach ◽

Learning Models ◽

Stroke Patients ◽

Local Approach ◽

Prediction Approach ◽

Machine Learning Models

Background An accurate prediction of tissue outcome in acute ischemic stroke patients is of high interest for treatment decision making. To date, various machine learning models have been proposed that combine multi-parametric imaging data for this purpose. However, most of these machine learning models were trained using voxel information extracted from the whole brain, without taking differences in susceptibility to ischemia into account that exist between brain regions. The aim of this study was to develop and evaluate a local tissue outcome prediction approach, which makes predictions using locally trained machine learning models and thus accounts for regional differences. Material and methods Multi-parametric MRI data from 99 acute ischemic stroke patients were used for the development and evaluation of the local tissue outcome prediction approach. Diffusion (ADC) and perfusion parameter maps (CBF, CBV, MTT, Tmax) and corresponding follow-up lesion masks for each patient were registered to the MNI brain atlas. Logistic regression (LR) and random forest (RF) models were trained employing a local approach, which makes predictions using models individually trained for each specific voxel position using the corresponding local data. A global approach, which uses a single model trained using all voxels of the brain, was used for comparison. Tissue outcome predictions resulting from the global and local RF and LR models, as well as a combined (hybrid) approach were quantitatively evaluated and compared using the area under the receiver operating characteristic curve (ROC AUC), the Dice coefficient, and the sensitivity and specificity metrics. Results Statistical analysis revealed the highest ROC AUC and Dice values for the hybrid approach. With 0.872 (ROC AUC; LR) and 0.353 (Dice; RF), these values were significantly higher (p < 0.01) than the values of the two other approaches. In addition, the local approach achieved the highest sensitivity of 0.448 (LR). Overall, the hybrid approach was only outperformed in sensitivity (LR) by the local approach and in specificity by both other approaches. However, in these cases the effect sizes were comparatively small. Conclusion The results of this study suggest that using locally trained machine learning models can lead to better lesion outcome prediction results compared to a single global machine learning model trained using all voxel information independent of the location in the brain.

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