MIDLMedical Imaging with Deep Learning 2022 Short Paper – MIDL 2022 submission
– Under Review Haoyue ZhangHaoyue Zhang and Jennifer S Polson contributed
equally.1,2<EMAIL_ADDRESS>
Jennifer S Polson1,2<EMAIL_ADDRESS>
Eric J<EMAIL_ADDRESS>
Kambiz Nael3<EMAIL_ADDRESS>
William Speier1,3<EMAIL_ADDRESS>
Corey W. Arnold1,2,3,4<EMAIL_ADDRESS>
1 Computational Diagnostics Lab, University of California, Los Angeles, CA
90024 USA
2 Department of Bioengineering, University of California, Los Angeles, CA
90024 USA
3 Department of Radiology, University of California, Los Angeles, CA 90024,
USA
4 Department of Pathology, University of California, Los Angeles, CA 90024,
USA

# Predicting Thrombectomy Recanalization from CT Imaging Using Deep Learning
Models

###### 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.

###### keywords:

Acute Ischemic Stroke, Computed Tomography, Deep Learning, Vision Transformers

††editors: Under Review for MIDL 2022

##  1 Introduction

Stroke is the fifth leading cause of death and the leading cause of long-term
disability; of the 795,000 new and recurrent strokes each year, acute ischemic
stroke (AIS) accounts for 87% of cases [Tsao et al.(2022)]. Mechanical
thrombectomy (MTB) is the leading treatment for patients with clots in large
blood vessels. In this procedure, a blood clot is surgically removed from an
artery to achieve recanalization, i.e., restored blood flow. As a standard
measurement for recanalization achieved, a modified treatment in cerebral
ischemia (mTICI) score [Tomsick(2007)] is assigned to patients post-treatment.
This post-treatment score is clinically significant, as it has been shown that
favorable scores, i.e., mTICI 2c or greater, are associated with better
clinical outcomes in the long term [Ángel Chamorro et al.(2017)]. Unfavorable
scores (mTICI less than 2c) indicate that the treatment did not effectively
clear the blood vessel. Imaging has been identified as one modality to
illustrate patient physiology that could influence the likelihood of a
successful MTB procedure. Predicting final mTICI score prior to a procedure
can provide doctors and with more more information when considering treatment
options. Deep learning has been shown to leverage the amount of detail in
images to improve prediction accuracy [LeCun et al.(2015)LeCun, Bengio, and
Hinton]. Current literature presents models that perform semi-automated
prediction of mTICI score based on pre-treatment CT imaging, with inconsistent
performance [Hilbert et al.(2019), Siddiqui et al.(2021)]. We propose a fully
automated model that uses both CT and CTA images to predict mTICI score post-
treatment, incorporating attention modules into a deep learning network to
effectively localize to informative stroke regions without requiring manual
segmentation.

##  2 Data and Methods

The cohort used for this study comprises patients treated from 2012-2019. A
patient was included in the cohort if they had CT and CTA imaging, underwent
thrombectomy for stroke, and were assigned an mTICI score post-MTB. Of the 254
eligible patients, 69 patients were excluded due to missing either CT or CTA
series, and 8 were excluded to due unclear stroke location, leaving 177
patients total. The dataset matched demographic distributions seen in other
stroke studies, and the target labels were approximately balanced. Patient
images were processed using a previously published pipeline adapted for CT,
which included brain extraction and registration to a CT template in MNI space
[Zhang et al.(2021b)].

Figure 1: Overview of the proposed deep learning architecture. The figure
details the entire architecture, neighborhood branch modules, spatial
attention transformer module (SAT), and cross attention transformer module
(CAT).

Utilizing a ResNet backbone, CT and CTA images were used as a slice-wise input
(input size 26x224x224) for a global 2D convolutional block, chosen because of
the large slice thickness found in stroke protocols [He et al.(2016)]. The
outputs from neighboring slices were then fed into ResNet34-based branches
that shared weights across slice neighborhoods. The model leveraged two
versions of transformer attention modules. A spatial attention transformer
(SAT) utilized multi-head attention on each slice to focus on salient regions
[Dosovitskiy et al.(2021)]. Within each neighborhood branch, a cross attention
transformer (CAT) identified important slices. Finally, the branch outputs
were subjected to a weighted softmax layer to ultimately generate binary
predictions. The architecture and modules are summarized in Figure 1. The
model was trained for 200 epochs with early stopping, using the Adam optimizer
with weight decay, a batch size of 12, and a learning rate of 0.0001.

Model | ROC-AUC | Accuracy | Precision | Sensitivity | Specificity
---|---|---|---|---|---
Siddiqui et al. | 0.717 | – | – | – | –
Hilber et al. |  0.65 $\pm$ 0.10 | – | – | – | –
Radiomics |  0.6859 $\pm$ 0.043 |  0.6877 $\pm$ 0.039 |  0.6417 $\pm$ 0.068 |  0.7425 $\pm$ 0.123 |  0.6421 $\pm$ 0.126
ResNet34 |  0.5840 $\pm$ 0.036 |  0.5656 $\pm$ 0.046 |  0.5410 $\pm$ 0.067 |  0.8500 $\pm$ 0.300 |  0.3253 $\pm$ 0.296
SCANet |  0.7732 $\pm$ 0.039 |  0.7523 $\pm$ 0.042 |  0.7424 $\pm$ 0.145 |  0.8250 $\pm$ 0.174 |  0.6905 $\pm$ 0.215
Table 1: Performance of our current model benchmarked against results from
literature as well as previously published models applied to this cohort
[Hilbert et al.(2019), Siddiqui et al.(2021)].

##  3 Results and Discussion

The results of our experiments are summarized in Table 1. The average ROC-AUC
achieved by SCANet was 0.7732 $\pm$ 0.039. This is a significant improvement
over the previously published fully automatic deep learning model[Siddiqui et
al.(2021)]. Our method also demonstrates higher and more robust performance
metrics than the state-of-the-art model requiring manual clot segmentation
[Hilbert et al.(2019)]. In addition to the literature benchmarks, SCANet
performs better than a radiomics-based model and standard deep learning
architecture when trained on the same cohort [Zhang et al.(2021a)].

Clinicians decide to perform MTB based on likelihood of successful
recanalization, but it is unknown what factors underlie MTB responses.
Clinical images such as CT and CTA contain valuable information to predict
procedure outcome, and deep learning models have the capability to learn
representations from highly dimensional imaging data. This study sought to
predict final MTB recanalization in a fully automatic manner, leveraging
recent advances in vision transformers to localize to the stroke region. We
showed that our proposed model outperforms prior fully- and semi-automated
machine and deep learning models. The primary limitation of our study is the
small sample size, which precludes more robust validation. A few future
directions include experimenting on a larger dataset across several
institutions, optimizing the preprocessing pipeline to more effectively
preserve high resolution CTA, and correlation of the immediate treatment
response with long-term outcomes. These steps can produce a model that more
accurately predicts MTB recanalization, in turn helping doctors and patients
in the treatment decision process.

## References

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  * [He et al.(2016)] Kaiming He et al.  Deep residual learning for image recognition.  In _Proceedings of the IEEE conference on computer vision and pattern recognition_ , pages 770–778, 2016.
  * [Hilbert et al.(2019)] Adam Hilbert et al.  Data-efficient deep learning of radiological image data for outcome prediction after endovascular treatment of patients with acute ischemic stroke.  _Computers in Biology and Medicine_ , 115:103516, 2019.
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  * [Tomsick(2007)] Thomas A. Tomsick.  Timi, tibi, tici: I came, i saw, i got confused.  _American Journal Of Neuroradiology_ , 28(2):382–384, 2007.
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  * [Zhang et al.(2021b)] Haoyue Zhang et al.  Intra-domain task-adaptive transfer learning to determine acute ischemic stroke onset time.  _Computerized Medical Imaging and Graphics_ , 90:101926, 2021b.
  * [Ángel Chamorro et al.(2017)] Ángel Chamorro et al.  Complete reperfusion is required for maximal benefits of mechanical thrombectomy in stroke patients.  _Scientific Reports_ , 7(1):11636, 2017.