J Stroke.  2021 May;23(2):234-243. 10.5853/jos.2020.05064.

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

• Qiu W
• Kuang H
• Ospel JM
• Hill MD
• Demchuk AM
• Goyal M
• Menon BK

• KMID: 2516412
• DOI: http://doi.org/10.5853/jos.2020.05064

Abstract

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; P=0.56; CCC 0.63 [95% confidence interval [CI], 0.53 to 0.71; P<0.01], ICC 0.68 [95% CI, 0.58 to 0.78; P<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; P<0.01) and ICC was 0.42 (95% CI, 0.18 to 0.50; P<0.01); in non-reperfused subjects CCC was 0.52 (95% CI, 0.20 to 0.60; P<0.001) and ICC was 0.60 (95% CI, 0.37 to 0.76; P<0.001). No difference was observed between the mCTA and CTP predicted infarct volume in the test cohort (P=0.67).
Conclusions
A ML based mCTA model is able to predict brain tissue perfusion abnormality and follow-up infarction, comparable to CTP.

Keyword

Ischemic stroke; Multiphase computed tomography angiography; Cerebral infarction; Perfusion; Machine learning

Figure

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  Figure 1. Patient inclusion chart. CTP, computed tomographic perfusion.

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  Figure 2. Training and testing strategy of machine learning models to predict core, penumbra and perfusion status. (A) Derivation and testing of penumbra model and infarction model using follow-up infarct as reference standard. (B) Derivation and testing of the perfusion model using time-dependent Tmax thresholded map as reference standard. mCTA, multiphase computed tomographic angiography.

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  Figure 3. Multiphase computed tomographic angiography (mCTA) predicted infarct map compared to computed tomographic perfusion (CTP) time-dependent Tmax thresholded map when compared to follow-up infarct. (A) Patient who achieved reperfusion (modified thrombolysis in cerebral infarction [mTICI] 2b), (B) patient who did not achieve reperfusion, and (C) patient who achieved reperfusion (mTICI 3). Columns: mCTA phase 1 to 3, mCTA predicted perfusion maps, mCTA predicted core (red in column 5) and penumbra (blue in column 5) overlaid on the mCTA predicted perfusion map, CTP Tmax maps, CTP time-dependent Tmax threshold predicted infarct, infarct contoured in follow-up imaging, respectively. The penumbra is shown as affected tissue from the penumbra model minus affected tissue from the core model.

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  Figure 4. Bland-Altman plots of (A) multiphase computed tomographic angiography (mCTA) infarct volume predicted using the penumbra model versus follow-up infarct volume for the 44 patients who did not achieve acute reperfusion; (B) mCTA infarct volume predicted using core model versus follow-up infarct volume for the 100 patients who achieved reperfusion; and (C) mCTA perfusion volume predicted using perfusion model versus time-dependent Tmax predicted infarct volume for all 144 patients in the test cohort. CTP, computed tomographic perfusion; SD, standard deviation.

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  Figure 5. An example shows the computed tomographic perfusion (CTP) maps (column 1–3) due to the excessive movement of the patient during CTP acquisition, versus multiphase computed tomographic angiography (mCTA) prediction (column 4) that correlates well with follow-up imaging (column 5). CBF, cerebral blood flow; CBV, cerebral blood volume.

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  Figure 6. An example shows the multiphase computed tomographic angiography (mCTA) prediction, computed tomographic perfusion map, and follow-up imaging of a patient with posterior circulation occlusion.

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  Figure 7. Failure cases from multiphase computed tomographic angiography (mCTA) prediction. (A) Row shows images from a patient who presented ultraearly with an onset-to computed tomography time of 21 minutes. The mCTA model significantly over-predicts follow-up infarct. (B) Row shows images from a patient without obvious occlusion; the mCTA model shows a false positive perfusion abnormality in the left posterior occipital region. (C) Row shows images of a patient with an internal carotid artery occlusion; the mCTA model under-estimates the perfusion abnormality. Column 1–3: mCTA predicted follow-up infarct, Tmax, and follow-up infarct imaging. NCCT, non-contrast-enhanced computed tomography; DWI, diffusion-weighted imaging.

Reference

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