Predicting Thrombectomy Recanalization from CT Imaging Using Deep Learning Models
This addresses the problem of treatment decision-making for clinicians in stroke care, but it is incremental as it applies a novel deep learning method to an existing medical imaging task.
The study tackled predicting successful recanalization after mechanical thrombectomy for acute ischemic stroke patients using pre-treatment CT and CTA imaging, achieving an average ROC-AUC of 77.33% with a deep learning model.
For acute ischemic stroke (AIS) patients with large vessel occlusions, clinicians must decide if the benefit of mechanical thrombectomy (MTB) outweighs the risks and potential complications following an invasive procedure. Pre-treatment computed tomography (CT) and angiography (CTA) are widely used to characterize occlusions in the brain vasculature. If a patient is deemed eligible, a modified treatment in cerebral ischemia (mTICI) score will be used to grade how well blood flow is reestablished throughout and following the MTB procedure. An estimation of the likelihood of successful recanalization can support treatment decision-making. In this study, we proposed a fully automated prediction of a patient's recanalization score using pre-treatment CT and CTA imaging. We designed a spatial cross attention network (SCANet) that utilizes vision transformers to localize to pertinent slices and brain regions. Our top model achieved an average cross-validated ROC-AUC of 77.33 $\pm$ 3.9\%. This is a promising result that supports future applications of deep learning on CT and CTA for the identification of eligible AIS patients for MTB.