Automated Prediction of Proximal Middle Cerebral Artery Occlusions in Noncontrast Brain Computed Tomography

Abstract

Background: Early identification of large vessel occlusion (LVO) in patients with ischemic stroke is crucial for timely interventions. We propose a machine learning-based algorithm (JLK-CTL) that uses handcrafted features from noncontrast computed tomography to predict LVO.

Methods: We included patients with ischemic stroke who underwent concurrent noncontrast computed tomography and computed tomography angiography in seven hospitals. Patients from 5 of these hospitals, admitted between May 2011 and March 2015, were randomly divided into training and internal validation (9:1 ratio). Those from the remaining 2 hospitals, admitted between March 2021 and September 2021, were designated for external validation. From each noncontrast computed tomography scan, we extracted differences in volume, tissue density, and Hounsfield unit distribution between bihemispheric regions (striatocapsular, insula, M1-M3, and M4-M6, modified from the Alberta Stroke Program Early Computed Tomography Score). A deep learning algorithm was used to incorporate clot signs as an additional feature. Machine learning models, including ExtraTrees, random forest, extreme gradient boosting, support vector machine, and multilayer perceptron, as well as a deep learning model, were trained and evaluated. Additionally, we assessed the models' performance after incorporating the National Institutes of Health Stroke Scale scores as an additional feature.

Results: Among 2919 patients, 83 were excluded. Across the training (n=2463), internal validation (n=275), and external validation (n=95) datasets, the mean ages were 68.5±12.4, 67.6±13.8, and 67.9±13.6 years, respectively. The proportions of men were 57%, 53%, and 59%, with LVO prevalences of 17.0%, 16.4%, and 26.3%, respectively. In the external validation, the ExtraTrees model achieved a robust area under the curve of 0.888 (95% CI, 0.850-0.925), with a sensitivity of 80.1% (95% CI, 72.0-88.1) and a specificity of 88.6% (95% CI, 84.7-92.5). Adding the National Institutes of Health Stroke Scale score to the ExtraTrees model increased sensitivity (from 80.1% to 92.1%) while maintaining specificity.

Conclusions: Our algorithm provides reliable predictions of LVO using noncontrast computed tomography. By enabling early LVO identification, our algorithm has the potential to expedite the stroke workflow.

Keywords: artificial intelligence; atrial fibrillation; computed tomography angiography; deep learning; ischemic stroke; predictive value of tests.

Figure 1.

Schematic explanation for JLK-CTL. By aligning a standard template with regional masks (striatocapsular, insula, M1–M3, and M4–M6, modified from Alberta Stroke Program Early Computed Tomography Score) onto noncontrast computed tomography scan, we calculated nonequivalence scores (non-eq score) indicating mean differences in Hounsfield units (HU) between corresponding regions on both sides. Furthermore, relative differences in tissue density, volume, and HU distribution between the right and left regions were calculated. These are indicated as net water uptake (NWU), change in volume (ΔVolume), and SD of HU distribution (Std), respectively. The existence of clot sign was included as an additional feature, encompassing 17 features. Outputs of ExtraTrees are represented as large vascular occlusion (LVO) scores. We offer a bar graph that illustrates factors that contribute to an increase (shown in red) or decrease (indicated in blue) in the LVO score.

Figure 2.

Receiver operating characteristic (ROC) curve of machine learning models and a deep learning (DL) model. A and B, Comparisons of the ROC curves of the DL model with those of machine learning models in the internal and external validation datasets, respectively. C and D, ROC curves of National Institutes of Health Stroke Scale (NIHSS) model, imaging-only model, and imaging plus NIHSS model in internal and external validation datasets, respectively. ExT indicates ExtraTrees; MLP, multilayer perceptron; RF, random forest; SVM, support vector machine; and XGB, extreme gradient boosting.

Figure 3.

Subgroup analysis stratified by onset to image (≤6 vs >6 hours) and National Institutes of Health Stroke Scale score (NIHSS score ≤11 vs >11) using JLK-CTL. A and B, Internal and external validations after stratified by onset to image. Red and green lines indicate the receiver operating characteristic curve of the onset-to-image ≤6-hour group and >6-hour group, respectively. C and D, Internal and external validations after stratified by NIHSS score. Red and green lines indicate the receiver operating characteristic curve of the NIHSS score ≤11 group and >11 group, respectively.