Rationale and Objectives
The measurement of the time since stroke onset (TSS) is crucial for decision-making
in the treatment of acute ischemic stroke (AIS). This study assessed the utility of
computed tomography angiography (CTA) radiomics features (RFs) to estimate TSS.
Materials and Methods
A total of 221 patients with AIS were enrolled in this retrospective study and were
divided into a training group (n = 154) and a test group (n = 67). Thrombi in CTA images were manually outlined using ITK-SNAP. Images were aligned,
normalized, and pre-processed to extract RFs. The TSS was calculated as the time from
stroke onset to CTA completion. The patients were classified into two groups according
to estimated TSS: ≤4.5 and >4.5 hours. A total of 944 RFs were extracted from CTA
images. Clinical factors associated with TSS were identified using multivariate logistic
regression, and a combined model (clinical data and RFs) was constructed. The predictive
value of the models was assessed by the area under the receiver operating characteristic
curve (AUC). The performance of the models was compared using the DeLong test, and
clinical utility was evaluated by decision curve analysis.
Results
The AUC of the radiomics model was 0.803 (95% confidence interval [CI]: 0.733-0.873)
and 0.803 (95% CI: 0.698-0.908) in the training and test cohorts, respectively. The
AUC of the combined model (containing data on age, diabetes, and atrial fibrillation)
in the training and test sets was 0.813 (95% CI: 0.750-0.889) and 0.803 (95% CI: 0.699-0.907),
respectively. The DeLong test showed no significant difference between the radiomics
and combined models. Decision curve analysis showed that both models had clinical
utility.
Conclusion
CTA-based thrombus radiomics can estimate TSS in patients with AIS. The addition of
clinical data to the model does not improve predictive performance.
Key Words
Abbreviations:
TSS (time since stroke onset), AIS (acute ischemic stroke), CTA (computed tomography angiography), RFs (radiomics features), AUC (area under the curve), DCA (decision curve analysis), CI (confidence interval), MRI (magnetic resonance imaging), ROI (region of interest), MCA (middle cerebral artery), HMCAS (hyperdense middle cerebral artery sign), NIHSS (national institutes of health stroke scale), CT (computed tomography), ICC (intraclass correlation coefficient), RFE (recursive feature elimination), SVM (support vector machine), ROC (receiver operating characteristic), PPV (positive predictive value), NPV (negative predictive value), ICA (internal carotid artery), GLCM (gray level co-occurrence matrix), GLRLM (gray level run length matrix), GLSZM (gray level size zone), GLDM (gray level dependence matrix), NGTDM (neighboring gray-tone difference matrix)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: January 23, 2023
Accepted:
December 18,
2022
Received in revised form:
December 11,
2022
Received:
September 25,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
