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 Successful recanalization using the Embolus Retriever with Interlinked Cage for acute stroke due to calcified cerebral emboli

2018 ◽  
Vol 24 (6) ◽  
pp. 674-677 ◽  
Author(s):  
Hyo S Kwak ◽  
Jung S Park
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Thrombolytic Agent ◽  
Mechanical Thrombectomy ◽  
Computed Tomographic Angiography ◽  
Sudden Onset ◽  
Aspiration Thrombectomy ◽  
Computed Tomographic ◽  
Tomographic Angiography ◽  
Left Middle

Mechanical thrombectomy is a safe and effective treatment in patients with acute ischemic stroke caused by large vessel occlusions. However, in rare cases, the procedure may be challenging due to the composition of the embolus. We describe a case of a mechanical thrombectomy with the Embolus Retriever with Interlinked Cage (ERIC) device in a patient with an acute ischemic stroke due to calcified cerebral emboli in the middle cerebral artery. The procedure was done after a failed recanalization attempt with manual aspiration thrombectomy. An 82-year-old woman presented to the emergency department with a sudden onset of right-sided weakness. A computed tomographic angiography showed left middle cerebral (M1 branch) calcified emboli. After the administration of an intravenous thrombolytic agent, the patient was transferred to the angiographic suite for a mechanical thrombectomy. After failure to recanalize the vessel with manual aspiration thrombectomy, successful recanalization was achieved via mechanical thrombectomy using the ERIC device. Mechanical thrombectomy with an ERIC device can be a useful option in cases of acute ischemic stroke caused by calcified cerebral emboli.

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 Midterm Clinical and Angiographic Follow-Up for the First Food and Drug Administration-Approved Prospective, Single-Arm Trial of Primary Stenting for Stroke: SARIS (Stent-Assisted Recanalization for Acute Ischemic Stroke)

Neurosurgery ◽  
2011 ◽  
Vol 69 (4) ◽  
pp. 915-920 ◽  
Author(s):  
Elad I Levy ◽  
Maryam Rahman ◽  
Alexander A Khalessi ◽  
Patrick T Beyer ◽  
Sabareesh K Natarajan ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Clinical Results ◽  
Computed Tomographic Angiography ◽  
Stent Deployment ◽  
Stent Patency ◽  
Stroke Treatment ◽  
Intracranial Stent ◽  
Computed Tomographic

Abstract BACKGROUND: Although early data demonstrate encouraging angiographic results following intracranial stent deployment for acute ischemic stroke, longer-term follow-up is necessary to evaluate the clinical outcomes, as well as the durability of angiographic results. OBJECTIVE: We report 6-month clinical and radiologic follow-up data of the 20 patients prospectively enrolled in the Stent-Assisted Recanalization in acute Ischemic Stroke (SARIS) trial. METHODS: Twenty patients were prospectively enrolled to receive self-expanding intra-arterial stents as first-line therapy for acute ischemic stroke treatment. Patients were scheduled for follow-up 6-months after treatment for clinical evaluation (modified Rankin Scale [mRS] score obtained by a trained certified research nurse/nurse practitioner) and repeat cerebral angiography. Angiographic interpretation was performed by an independent adjudicator. RESULTS: At 6 months, the mRS score was ≤3 in 60% of patients (n = 12) and was ≤2 in 55% of patients (n = 11). Mortality at the 6-month follow-up was 35% (n = 7). Follow-up angiography was performed for 85% (11 of 13) of surviving patients. All patients undergoing angiographic follow-up demonstrated Thrombolysis in Myocardial Infarction 3 flow on digital subtraction angiography or stent patency on computed tomographic angiography. None of the patients demonstrated evidence of in-stent stenosis (≥50% vessel narrowing). CONCLUSION: The midterm angiographic and clinical results following intracranial stent deployment for acute ischemic stroke are encouraging. Further study of primary stent-for-stroke treatment is warranted.

scholarly journals Additional Value of Machine-Learning Computed Tomographic Angiography-Based Fractional Flow Reserve Compared to Standard Computed Tomographic Angiography

2020 ◽  
Vol 9 (3) ◽  
pp. 676
Author(s):  
Dirk Lossnitzer ◽  
Leonard Chandra ◽  
Marlon Rutsch ◽  
Tobias Becher ◽  
Daniel Overhoff ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Fractional Flow Reserve ◽  
Predictive Value ◽  
Computed Tomographic Angiography ◽  
Fractional Flow ◽  
Computed Tomographic ◽  
Coronary Syndrome ◽  
Flow Reserve ◽  
Tomographic Angiography

Background: Machine-learning-based computed-tomography-derived fractional flow reserve (CT-FFRML) obtains a hemodynamic index in coronary arteries. We examined whether it could reduce the number of invasive coronary angiographies (ICA) showing no obstructive lesions. We further compared CT-FFRML-derived measurements to clinical and CT-derived scores. Methods: We retrospectively selected 88 patients (63 ± 11years, 74% male) with chronic coronary syndrome (CCS) who underwent clinically indicated coronary computed tomography angiography (cCTA) and ICA. cCTA image data were processed with an on-site prototype CT-FFRML software. Results: CT-FFRML revealed an index of >0.80 in coronary vessels of 48 (55%) patients. This finding was corroborated in 45 (94%) patients by ICA, yet three (6%) received revascularization. In patients with an index ≤ 0.80, three (8%) of 40 were identified as false positive. A total of 48 (55%) patients could have been retained from ICA. CT-FFRML (AUC = 0.96, p ≤ 0.0001) demonstrated a higher diagnostic accuracy compared to the pretest probability or CT-derived scores and showed an excellent sensitivity (93%), specificity (94%), positive predictive value (PPV; 93%) and negative predictive value (NPV; 94%). Conclusion: CT-FFRML could be beneficial for clinical practice, as it may identify patients with CAD without hemodynamical significant stenosis, and may thus reduce the rate of ICA without necessity for coronary intervention.

Abstract TP69: Three-Phase Helical Computed Tomographic Angiography Perfusion Maps Predict Final Infarction in the Acute Ischemic Stroke Setting

Stroke ◽  
2019 ◽  
Vol 50 (Suppl_1) ◽  
Author(s):  
Connor C McDougall ◽  
Erin Maxwell ◽  
Noaah Reaume ◽  
Rani Gupta Sah ◽  
Christopher D d'Esterre ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Computed Tomographic Angiography ◽  
Computed Tomographic ◽  
Three Phase ◽  
Tomographic Angiography ◽  
Perfusion Maps
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