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. 2021 Jan;52(1):223-231.
doi: 10.1161/STROKEAHA.120.030092. Epub 2020 Dec 7.

Computed Tomography Perfusion-Based Machine Learning Model Better Predicts Follow-Up Infarction in Patients With Acute Ischemic Stroke

Hulin Kuang  1 , Wu Qiu  1 , Anna M Boers  2 , Scott Brown  3 , Keith Muir  4 , Charles B L M Majoie  5   6 , Diederik W J Dippel  7 , Phil White  8 , Jonathan Epstein  9 , Peter J Mitchell  10 , Antoni Dávalos  11 , Serge Bracard  12   13 , Bruce Campbell  14 , Jeffrey L Saver  15 , Tudor G Jovin  16 , Marta Rubiera  17 , Alexander V Khaw  18 , Jai J Shankar  19 , Enrico Fainardi  20 , Michael D Hill  1   21   22 , Andrew M Demchuk  1   21   22 , Mayank Goyal  1   21   22 , Bijoy K Menon  1   21   22
Affiliations

Computed Tomography Perfusion-Based Machine Learning Model Better Predicts Follow-Up Infarction in Patients With Acute Ischemic Stroke

Hulin Kuang et al. Stroke. 2021 Jan.

Abstract

Background and purpose: Prediction of infarct extent among patients with acute ischemic stroke using computed tomography perfusion is defined by predefined discrete computed tomography perfusion thresholds. Our objective is to develop a threshold-free computed tomography perfusion-based machine learning (ML) model to predict follow-up infarct in patients with acute ischemic stroke.

Methods: Sixty-eight patients from the PRoveIT study (Measuring Collaterals With Multi-Phase CT Angiography in Patients With Ischemic Stroke) were used to derive a ML model using random forest to predict follow-up infarction voxel by voxel, and 137 patients from the HERMES study (Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials) were used to test the derived ML model. Average map, Tmax, cerebral blood flow, cerebral blood volume, and time variables including stroke onset-to-imaging and imaging-to-reperfusion time, were used as features to train the ML model. Spatial and volumetric agreement between the ML model predicted follow-up infarct and actual follow-up infarct were assessed. Relative cerebral blood flow <0.3 threshold using RAPID software and time-dependent Tmax thresholds were compared with the ML model.

Results: In the test cohort (137 patients), median follow-up infarct volume predicted by the ML model was 30.9 mL (interquartile range, 16.4-54.3 mL), compared with a median 29.6 mL (interquartile range, 11.1-70.9 mL) of actual follow-up infarct volume. The Pearson correlation coefficient between 2 measurements was 0.80 (95% CI, 0.74-0.86, P<0.001) while the volumetric difference was -3.2 mL (interquartile range, -16.7 to 6.1 mL). Volumetric difference with the ML model was smaller versus the relative cerebral blood flow <0.3 threshold and the time-dependent Tmax threshold (P<0.001).

Conclusions: A ML using computed tomography perfusion data and time estimates follow-up infarction in patients with acute ischemic stroke better than current methods.

Keywords: acute ischemic stroke; computed tomographic perfusion; infarction; machine learning.

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