scholarly journals Automated Prediction of Ischemic Brain Tissue Fate from Multi-Phase CT-Angiography in Patients with Acute Ischemic Stroke Using Machine Learning

2020 ◽  
Author(s):  
Wu Qiu ◽  
Hulin Kuang ◽  
Johanna Ospel ◽  
Michael D Hill ◽  
Andrew Demchuk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Correlation Coefficient ◽  
Ct Angiography ◽  
Tissue Perfusion ◽  
Dice Similarity Coefficient ◽  
Derivation Cohort ◽  
Multiphase Ct

Background: Multiphase CT-Angiography (mCTA) provides time variant images of the pial vasculature supplying brain in patients with acute ischemic stroke (AIS). To develop a machine learning (ML) technique to predict infarct, penumbra and tissue perfusion from mCTA source images. Methods: 284 patients with AIS were included from the PRoveIT study. All patients had non-contrast CT, mCTA and CTP imaging at baseline and follow up MRI/NCCT imaging. Of the 284 patient images, 140 patient images were randomly selected to train and validate three ML models to predict infarct, penumbra, and perfusion parameter on CTP, respectively. The remaining unseen 144 patient images independent of the derivation cohort were used to test the derived ML models. The predicted infarct, penumbra, and perfusion volume from ML models was spatially and volumetrically compared to manually contoured follow up infarct and time-dependent Tmax thresholded volume (CTP volume), using Bland-Altman plots, concordance correlation coefficient (CCC), intra-class correlation coefficient (ICC), and Dice similarity coefficient (DSC). Results: Within the test cohort, Bland-Altman plots showed that the mean difference between the mCTA predicted infarct and follow up infarct was 21.7 mL (limit of agreement (LoA): -41.0 to 84.3mL) in the 100 patients who had acute reperfusion (mTICI 2b/2c/3), and 3.4mL (LoA: -66 to 72.9mL) in the 44 patients who did not achieve reperfusion (mTICI 0/1). Amongst reperfused subjects, CCC was 0.4 [95%CI: 0.15-0.55, P<.01] and ICC 0.42 [95% CI: 0.18-0.50, P<.01]; in non-reperfused subjects CCC was 0.52 [95%CI: 0.2-0.6, P<.001] and ICC 0.6 [95% CI: 0.37-0.76, P<.001]. No difference was observed between the mCTA and CTP predicted infarct volume for the overall test cohort (P=.67). Conclusion: Multiphase CT Angiography is able to predict infarct, penumbra and tissue perfusion, comparable to CT perfusion imaging.

scholarly journals Automated Prediction of Ischemic Brain Tissue Fate from Multiphase Computed Tomographic Angiography in Patients with Acute Ischemic Stroke Using Machine Learning

2021 ◽  
Vol 23 (2) ◽  
pp. 234-243
Author(s):  
Wu Qiu ◽  
Hulin Kuang ◽  
Johanna M. Ospel ◽  
Michael D. Hill ◽  
Andrew M. Demchuk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Tissue Perfusion ◽  
Computed Tomographic Angiography ◽  
Perfusion Abnormality ◽  
Computed Tomographic ◽  
Tomographic Angiography

Background and Purpose Multiphase computed tomographic angiography (mCTA) provides time variant images of pial vasculature supplying brain in patients with acute ischemic stroke (AIS). To develop a machine learning (ML) technique to predict tissue perfusion and infarction from mCTA source images.Methods 284 patients with AIS were included from the Precise and Rapid assessment of collaterals using multi-phase CTA in the triage of patients with acute ischemic stroke for Intra-artery Therapy (Prove-IT) study. All patients had non-contrast computed tomography, mCTA, and computed tomographic perfusion (CTP) at baseline and follow-up magnetic resonance imaging/non-contrast-enhanced computed tomography. Of the 284 patient images, 140 patient images were randomly selected to train and validate three ML models to predict a pre-defined Tmax thresholded perfusion abnormality, core and penumbra on CTP. The remaining 144 patient images were used to test the ML models. The predicted perfusion, core and penumbra lesions from ML models were compared to CTP perfusion lesion and to follow-up infarct using Bland-Altman plots, concordance correlation coefficient (CCC), intra-class correlation coefficient (ICC), and Dice similarity coefficient.Results Mean difference between the mCTA predicted perfusion volume and CTP perfusion volume was 4.6 mL (limit of agreement [LoA], –53 to 62.1 mL; <i>P</i>=0.56; CCC 0.63 [95% confidence interval [CI], 0.53 to 0.71; <i>P</i><0.01], ICC 0.68 [95% CI, 0.58 to 0.78; <i>P</i><0.001]). Mean difference between the mCTA predicted infarct and follow-up infarct in the 100 patients with acute reperfusion (modified thrombolysis in cerebral infarction [mTICI] 2b/2c/3) was 21.7 mL, while it was 3.4 mL in the 44 patients not achieving reperfusion (mTICI 0/1). Amongst reperfused subjects, CCC was 0.4 (95% CI, 0.15 to 0.55; <i>P</i><0.01) and ICC was 0.42 (95% CI, 0.18 to 0.50; <i>P</i><0.01); in non-reperfused subjects CCC was 0.52 (95% CI, 0.20 to 0.60; <i>P</i><0.001) and ICC was 0.60 (95% CI, 0.37 to 0.76; <i>P</i><0.001). No difference was observed between the mCTA and CTP predicted infarct volume in the test cohort (<i>P</i>=0.67).Conclusions A ML based mCTA model is able to predict brain tissue perfusion abnormality and follow-up infarction, comparable to CTP.

scholarly journals Multiphase CT Angiography: A New Tool for the Imaging Triage of Patients with Acute Ischemic Stroke

Radiology ◽  
2015 ◽  
Vol 275 (2) ◽  
pp. 510-520 ◽  
Author(s):  
Bijoy K. Menon ◽  
Christopher D. d’Esterre ◽  
Emmad M. Qazi ◽  
Mohammed Almekhlafi ◽  
Leszek Hahn ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Ct Angiography ◽  
Multiphase Ct

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

Biomedicines ◽  
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.

scholarly journals Validation of collateral scoring on flat-detector multiphase CT angiography in patients with acute ischemic stroke

PLoS ONE ◽  
2018 ◽  
Vol 13 (8) ◽  
pp. e0202592 ◽  
Author(s):  
Ilko L. Maier ◽  
Fabien Scalzo ◽  
Johanna R. Leyhe ◽  
Katharina Schregel ◽  
Daniel Behme ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Ct Angiography ◽  
Flat Detector ◽  
Multiphase Ct

Total and regional ASPECT score for non-contrast CT, CT angiography, and CT perfusion: inter-rater agreement and its association with the final infarction in acute ischemic stroke patients

2021 ◽  
pp. 028418512110290
Author(s):  
Yue Chu ◽  
Gao Ma ◽  
Xiao-Quan Xu ◽  
Shan-Shan Lu ◽  
Yue-Zhou Cao ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Caudate Nucleus ◽  
Ct Angiography ◽  
Ct Perfusion ◽  
Internal Capsule ◽  
Rater Agreement ◽  
Caudate Nuclei

Background Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a grading system to assess the extent and distribution of early ischemic changes. Purpose To assess inter-rater agreement for total and regional ASPECTS on non-contrast computed tomography (NCCT) images, CT angiography source images (CTA-SI), and CT-perfusion cerebral blood volume (CTP-CBV) maps, and their association with final infarction in patients with acute ischemic stroke (AIS). Material and Methods A total of 96 consecutive patients with AIS who underwent pre-treatment NCCT and CTP were retrospectively enrolled. CTA-SI was reconstructed using the raw data of CTP. Total and regional ASPECTS were assessed on baseline NCCT, CTA-SI, and CTP-CBV, and on follow-up NCCT or diffusion-weighted imaging. Follow-up ASPECTS served as the reference standard for final infarction. Results CTP-CBV demonstrated higher concordance for total ASPECTS (interclass correlation coefficient, 0.895 vs. 0.771 vs. 0.777) and regional ASPECTS in internal capsule, lentiform, caudate nuclei, M5 and M6, compared with NCCT and CTA-SI. CTP-CBV showed a trend of stronger correlation with final ASPECTS than NCCT and CTA-SI (0.717 vs. 0.711 vs. 0.565; P > 0.05). ASPECTS in the internal capsule (ρ, 0.756 vs. 0.556; P = 0.016) and caudate nucleus (ρ, 0.717 vs. 0.476; P = 0.010) on CTP-CBV were more strongly correlated with follow-up ASPECTS than NCCT. CTP-CBV showed higher accuracy for predicting final infarction in the internal capsule (92.5% vs. 90.3% and 87.1%; P > 1.000, P = 0.125, respectively) and caudate nucleus (87.1% vs. 79.6% and 77.4%; P = 0.453, P = 0.039, respectively) than CTA-SI and NCCT. Conclusion CTP-CBV ASPECTS might be more reliable for delineating early ischemic changes and predicting final infarction.

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