Automated functional outcome prediction in stroke using combined imaging and clinical parameters

Abstract

Predicting functional outcome after intra-arterial treatment (IAT) in acute ischemic stroke (AIS) patients is an important aspect of treatment decision making and prognostics. Standard methods for functional outcome prediction after stroke combine baseline clinical (and radiological) parameters. In this study, we investigated to what extent baseline CTA images can be used for the prediction of functional outcome and how this relates to standard scoring methods. Furthermore, it was investigated whether combining baseline CTA images with clinical parameters improved the predictive accuracy compared to outcome prediction based on clinical parameters. We proposed two network architectures, a convolutional neural network (CNN) for the processing of image data and a multilayer perceptron for the processing of clinical (and radiological) parameters. Various training strategies were applied for the fusion of image and clinical data. The CNN processing CTA images achieved an average cross-validated area under the curve (AUC) score of 0.67, which was lower than for models processing clinical (and radiological) parameters. The best performing model combining CTA images and clinical parameters was trained end-to-end and applied weight initialization of the pre-trained CNN (AUC = 0.78). The DeLong test showed that the combined model performed significantly better than the model processing clinical parameters (AUC = 0.75). However, the difference is small and might not be clinically relevant. Compared to scoring methods processing clinical and radiological parameters the combined model achieved similar performance.