Predicting Thrombectomy Recanalization from CT Imaging Using Deep Learning Models

TL;DR

This paper introduces a deep learning model called SCANet that predicts the success of thrombectomy recanalization in stroke patients using pre-treatment CT imaging, aiding clinical decision-making.

Contribution

The study presents a novel spatial cross attention network leveraging vision transformers for automated recanalization prediction from CT scans.

Findings

Achieved an average ROC-AUC of 77.33% in cross-validation.

Demonstrated potential for deep learning to assist in stroke treatment decisions.

Supports future development of AI tools for AIS patient assessment.

Abstract

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.