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Computed Tomography Angiography-Based Thrombus Radiomics for Predicting the Time Since Stroke Onset

Published:January 23, 2023DOI:https://doi.org/10.1016/j.acra.2022.12.032

      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)
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