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

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, T max , 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 T max 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 T max 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.

Performance of Machine Learning for Tissue Outcome Prediction in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis

“…Thirteen studies adopted conventional ML algorithms including k-nearest neighbor classification (24), general linear regression (47), random forest (13,15,25,34,36,38,41,48) and gradient boosting (11,26,36) classifiers. Twentyfive studies proposed DL-based approaches consisting of artificial neural network (ANN) (31) and various types of convolutional neural network (CNN) with some of the noteworthy popular architectures, including 2D and 3D U-Net (12,16,17,27,28,39,40,43,49,50), residual network (ResNet) (12,29,37,50), recurrent residual U-Net (R2U-Net) (52) and DeepMedic (32).…”

“…It also drives the emergence of deep learning (DL) subfield, which has shown impressive results in medical image processing without prior selection for relevant features (9,10). Given the suboptimal performance of the conventional thresholding methods, initial studies attempted to apply ML and DL-based approaches and showed clear advantages for more precise prediction of the final infarct lesion from baseline imaging (11)(12)(13)(14)(15)(16)(17). These promising results inspired investigators to propose novel model methodologies by improving algorithm architectures, combining multi-modality input parameters, and applying in different clinical scenarios.…”

Prediction of Stroke Infarct Growth Rates by Baseline Perfusion Imaging

“…Previously published DL models made use of the different perfusion parameters obtained by deconvolution as input parameters or the output derived from deconvolution as ground truth. 18,[32][33][34][35] Our model is completely free of deconvolution by using the source perfusion images as input and the final infarct as ground truth, which is a great advantage. In previous research, we showed that source perfusion scans as input parameters outperformed perfusion parameters derived from deconvolution in predicting final infarct volume.…”

Multi-Mode Imaging Scale for Endovascular Therapy in Patients with Acute Ischemic Stroke (META)

“…Clinicians may be concerned about the time consumption of processing and assessment of image data. Artificial intelligence (AI) technology, which is a rapidly burgeoning field, can provide fast and efficient automatic imaging analysis Through machine learning, a recent study demonstrated that CTP data could be used to estimate follow-up infarct in AIS patients [32]. The existing commercial software RAPID can automatically calculate the ASPECTS in AIS patients [33].…”