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

Published:January 23, 2023DOI:

      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.


      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.


      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


      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

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Academic Radiology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Campbell BCV
        • Khatri P.
        Lancet. 2020; 396: 129-142
        • Powers WJ
        • Rabinstein AA
        • Ackerson T
        • et al.
        Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association.
        Stroke. 2019; 50: e344-e418
        • Mendelson SJ
        • Prabhakaran S.
        Diagnosis and management of transient ischemic attack and acute ischemic stroke: a review.
        JAMA. 2021; 325: 1088-1098
        • Thomalla G
        • Simonsen CZ
        • Boutitie F
        • et al.
        MRI-guided thrombolysis for stroke with unknown time of onset.
        N Engl J Med. 2018; 379: 611-622
        • Heit JJ
        • Sussman ES
        • Wintermark M.
        Perfusion computed tomography in acute ischemic stroke.
        Radiol Clin North Am. 2019; 57: 1109-1116
        • Thomalla G
        • Cheng B
        • Ebinger M
        • et al.
        DWI-FLAIR mismatch for the identification of patients with acute ischaemic stroke within 4.5 h of symptom onset (PRE-FLAIR): a multicentre observational study.
        Lancet Neurol. 2011; 10: 978-986
        • Berge E
        • Whiteley W
        • Audebert H
        • et al.
        European Stroke Organisation (ESO) guidelines on intravenous thrombolysis for acute ischaemic stroke.
        Eur Stroke J. 2021; 6: I-LXII
        • Lambin P
        • Rios-Velazquez E
        • Leijenaar R
        • et al.
        Radiomics: extracting more information from medical images using advanced feature analysis.
        Eur J Cancer. 2012; 48: 441-446
        • Gillies RJ
        • Anderson AR
        • Gatenby RA
        • et al.
        The biology underlying molecular imaging in oncology: from genome to anatome and back again.
        Clin Radiol. 2010; 65: 517-521
        • Gillies RJ
        • Kinahan PE
        • Hricak H.
        Radiomics: images are more than Pictures, they are data.
        Radiology. 2016; 278: 563-577
        • Chen Q
        • Xia T
        • Zhang M
        • et al.
        Radiomics in stroke neuroimaging: techniques, applications, and challenges.
        Aging Dis. 2021; 12: 143-154
        • Dutra BG
        • Tolhuisen ML
        • Alves H
        • et al.
        Thrombus imaging characteristics and outcomes in acute ischemic stroke patients undergoing endovascular treatment.
        Stroke. 2019; 50: 2057-2064
        • Yoo J
        • Baek JH
        • Park H
        • et al.
        Thrombus volume as a predictor of nonrecanalization after intravenous thrombolysis in acute stroke.
        Stroke. 2018; 49: 2108-2115
        • Qiu W
        • Kuang H
        • Nair J
        • et al.
        Radiomics-based intracranial thrombus features on CT and CTA predict recanalization with intravenous alteplase in patients with acute ischemic stroke.
        AJNR Am J Neuroradiol. 2019; 40: 39-44
        • Jiang J
        • Wei J
        • Zhu Y
        • et al.
        Clot-based radiomics model for cardioembolic stroke prediction with CT imaging before recanalization: a multicenter study.
        Eur Radiol. 2022;
        • Hofmeister J
        • Bernava G
        • Rosi A
        • et al.
        Clot-based radiomics predict a mechanical thrombectomy strategy for successful recanalization in acute ischemic stroke.
        Stroke. 2020; 51: 2488-2494
        • Leys D
        • Pruvo JP
        • Godefroy O
        • et al.
        Prevalence and significance of hyperdense middle cerebral artery in acute stroke.
        Stroke. 1992; 23: 317-324
        • Kwah LK
        • Diong J.
        National Institutes of Health Stroke Scale (NIHSS).
        J Physiother. 2014; 60: 61
        • Romano A
        • Biraschi F
        • Tavanti F
        • et al.
        Role of multidetector CT in the recognition of hyperdense middle cerebral artery sign (HMCAS) in patients with acute cerebral ischaemia: correlation with DWI-MRI sequences and clinical data.
        Radiol Med. 2015; 120: 222-227
        • Liebeskind DS.
        Collateral circulation.
        Stroke. 2003; 34: 2279-2284
        • Tan IY
        • Demchuk AM
        • Hopyan J
        • et al.
        CT angiography clot burden score and collateral score: correlation with clinical and radiologic outcomes in acute middle cerebral artery infarct.
        AJNR Am J Neuroradiol. 2009; 30: 525-531
        • Song Y
        • Zhang J
        • Zhang YD
        • et al.
        FeAture Explorer (FAE): a tool for developing and comparing radiomics models.
        PLoS One. 2020; 15e0237587
        • Cheng X
        • Wu H
        • Shi J
        • et al.
        ASPECTS-based net water uptake as an imaging biomarker for lesion age in acute ischemic stroke.
        J Neurol. 2021; 268: 4744-4751
        • Broocks G
        • Leischner H
        • Hanning U
        • et al.
        Lesion age imaging in acute stroke: water uptake in CT versus DWI-FLAIR mismatch.
        Ann Neurol. 2020; 88: 1144-1152
        • Lee H
        • Lee EJ
        • Ham S
        • et al.
        Machine learning approach to identify stroke within 4.5 hours.
        Stroke. 2020; 51: 860-866
        • Murray NM
        • Unberath M
        • Hager GD
        • et al.
        Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review.
        J Neurointerv Surg. 2020; 12: 156-164
        • Tomaszewski MR
        • Gillies RJ.
        The biological meaning of radiomic features.
        Radiology. 2021; 298: 505-516
        • van Lammeren GW
        • den Ruijter HM
        • Vrijenhoek JE
        • et al.
        Time-dependent changes in atherosclerotic plaque composition in patients undergoing carotid surgery.
        Circulation. 2014; 129: 2269-2276
        • Kim YD
        • Nam HS
        • Kim SH
        • et al.
        Time-dependent thrombus resolution after tissue-type plasminogen activator in patients with stroke and mice.
        Stroke. 2015; 46: 1877-1882
        • Pikija S
        • Magdic J
        • Trkulja V
        • et al.
        Intracranial thrombus morphology and composition undergoes time-dependent changes in acute ischemic stroke: a CT densitometry study.
        Int J Mol Sci. 2016; 17
        • Tolhuisen ML
        • Kappelhof M
        • Dutra BG
        • et al.
        Influence of onset to imaging time on radiological thrombus characteristics in acute ischemic stroke.
        Front Neurol. 2021; 12693427
        • Yao X
        • Mao L
        • Lv S
        • et al.
        CT radiomics features as a diagnostic tool for classifying basal ganglia infarction onset time.
        J Neurol Sci. 2020; 412116730
        • Wen X
        • Shu Z
        • Li Y
        • et al.
        Developing a model for estimating infarction onset time based on computed tomography radiomics in patients with acute middle cerebral artery occlusion.
        BMC Med Imaging. 2021; 21: 147
        • Zhang YQ
        • Liu AF
        • Man FY
        • et al.
        MRI radiomic features-based machine learning approach to classify ischemic stroke onset time.
        J Neurol. 2022; 269: 350-360