Semi-automatic quantitative measurements of intracranial internal carotid artery stenosis and calcification using CT angiography

Neuroradiology. 2012 Sep;54(9):919-27. doi: 10.1007/s00234-011-0998-0. Epub 2011 Dec 30.

Abstract

Introduction: Intracranial carotid artery atherosclerotic disease is an independent predictor for recurrent stroke. However, its quantitative assessment is not routinely performed in clinical practice. In this diagnostic study, we present and evaluate a novel semi-automatic application to quantitatively measure intracranial internal carotid artery (ICA) degree of stenosis and calcium volume in CT angiography (CTA) images.

Methods: In this retrospective study involving CTA images of 88 consecutive patients, intracranial ICA stenosis was quantitatively measured by two independent observers. Stenoses were categorized with cutoff values of 30% and 50%. The calcification in the intracranial ICA was qualitatively categorized as absent, mild, moderate, or severe and quantitatively measured using the semi-automatic application. Linear weighted kappa values were calculated to assess the interobserver agreement of the stenosis and calcium categorization. The average and the standard deviation of the quantitative calcium volume were calculated for the calcium categories.

Results: For the stenosis measurements, the CTA images of 162 arteries yielded an interobserver correlation of 0.78 (P < 0.001). Kappa values of the categorized stenosis measurements were moderate: 0.45 and 0.58 for cutoff values of 30% and 50%, respectively. The kappa value for the calcium categorization was 0.62, with a good agreement between the qualitative and quantitative calcium assessment.

Conclusions: Quantitative degree of stenosis measurement of the intracranial ICA on CTA is feasible with a good interobserver agreement ICA. Qualitative calcium categorization agrees well with quantitative measurements.

MeSH terms

  • Aged
  • Calcinosis / diagnostic imaging*
  • Carotid Stenosis / diagnostic imaging*
  • Cerebral Angiography / methods*
  • Female
  • Humans
  • Male
  • Pattern Recognition, Automated
  • Retrospective Studies
  • Tomography, X-Ray Computed / methods*