Machine Learning-Based Prediction of Brain Tissue Infarction in Patients With Acute Ischemic Stroke Treated With Theophylline as an Add-On to Thrombolytic Therapy: A Randomized Clinical Trial Subgroup Analysis

Abstract

Background and Purpose: The theophylline in acute ischemic stroke trial investigated the neuroprotective effect of theophylline as an add-on to thrombolytic therapy in patients with acute ischemic stroke. The aim of this pre-planned subgroup analysis was to use predictive modeling to virtually test for differences in the follow-up lesion volumes.Materials and Methods: A subgroup of 52 patients from the theophylline in acute ischemic stroke trial with multi-parametric MRI data acquired at baseline and at 24-h follow-up were analyzed. A machine learning model using voxel-by-voxel information from diffusion- and perfusion-weighted MRI and clinical parameters was used to predict the infarct volume for each individual patient and both treatment arms. After training of the two predictive models, two virtual lesion outcomes were available for each patient, one lesion predicted for theophylline treatment and one lesion predicted for placebo treatment.Results: The mean predicted volume of follow-up lesions was 11.4 ml (standard deviation 18.7) for patients virtually treated with theophylline and 11.2 ml (standard deviation 17.3) for patients virtually treated with placebo (p = 0.86).Conclusions: The predicted follow-up brain lesions for each patient were not significantly different for patients virtually treated with theophylline or placebo, as an add-on to thrombolytic therapy. Thus, this study confirmed the lack of neuroprotective effect of theophylline shown in the main clinical trial and is contrary to the results from preclinical stroke models.

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

“…The mean Dice values comparing the standard predictions to their corresponding ground-truths were not significantly different between the IAR, IVR, and NR machine learning models (p = 0.90). All three models produced mean Dice values > 0.44, which is comparable to similar tissue outcome prediction studies in the field of acute ischemic stroke [15][16][17][18][19][20][21][22][23]. Additionally, the proportional error, which quantifies the difference between the predicted and true lesion volume as a proportion of the true lesion volume, did not differ significantly between models (p = 0.60).…”

“…Methods using machine learning to better interpret imaging parameters for stroke outcome prediction have already been proposed in a number of independent studies [15][16][17][18][19][20][21][22][23] and international challenges [30]. These methods typically consider the full range of imaging features previously used in clinical practice as salient predictors of tissue outcome.…”

“…However, no such model exists for the evaluation of stroke treatments due to the high complexity of the cerebrovascular system and interventional procedure, which renders an in silico validation, for example using computational fluid dynamics simulations, infeasible. Instead, we propose the use of machine learning models, which have been used previously to model the evolution of acute ischemic stroke under particular treatment conditions [15][16][17][18][19][20][21][22][23]. In these studies, real patient data were used to empirically and automatically optimize (train) the machine learning model for the purpose of predicting patients' final voxel-wise tissue outcome given their baseline imaging and clinical information.…”

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

“…All except two studies (45,48) reported validation methods for the proposed model including using an independent test set, k-fold cross-validation, and leave-one-out cross-validation.…”

“…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).…”