E-ISSN: 1305-3612
Spectral segmentation and radiomic features predict carotid stenosis and ipsilateral ischemic burden from DECT angiography
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Neuroradiology - Original Article
VOLUME: 28 ISSUE: 3
P: 264-274
May 2022

Spectral segmentation and radiomic features predict carotid stenosis and ipsilateral ischemic burden from DECT angiography

Diagn Interv Radiol 2022;28(3):264-274
DOI: 10.5152/dir.2022.20842
Shadi Ebrahimian 1,2
Fatemeh Homayounieh 1,2
Ramandeep Singh 1
Andrew Primak 3
Mannudeep K. Kalra 1,2
Javier M. Romero 1
1. Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, Massachusetts, USA
2. MGH Webster Center for Quality and Safety, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
3. Siemens Healthcare USA Inc., Malvern, Pennsylvania, USA
No information available.
No information available
Received Date: 06.10.2020
Accepted Date: 12.04.2021
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ABSTRACT

PURPOSE

The purpose of this study is to compare spectral segmentation, spectral radiomic, and single- energy radiomic features in the assessment of internal and common carotid artery (ICA/CCA) stenosis and prediction of surgical outcome.

METHODS

Our ethical committee–approved, Health Insurance Portability and Accountability Act (HIPAA)- compliant study included 85 patients (mean age, 73 ± 10 years; male : female, 56 : 29) who under- went contrast-enhanced, dual-source dual-energy CT angiography (DECTA) (Siemens Definition Flash) of the neck for assessing ICA/CCA stenosis. Patients with a prior surgical or interventional treatment of carotid stenosis were excluded. Two radiologists graded the severity of carotid ste- nosis on DECTA images as mild (<50% luminal narrowing), moderate (50%-69%), and severe (>70%) stenosis. Thin-section, low- and high-kV DICOM images from the arterial phase acquisi- tion were processed with a dual-energy CT prototype (DTA, eXamine, Siemens Healthineers) to generate spectral segmentation and radiomic features over regions of interest along the entire length (volume) and separately at a single-section with maximum stenosis. Multiple logistic regressions and area under the receiver operating characteristic curve (AUC) were used for data analysis.

RESULTS

Among 85 patients, 22 ICA/CCAs had normal luminal dimensions and 148 ICA/CCAs had luminal stenosis (mild stenosis: 51, moderate: 38, severe: 59). For differentiating non-severe and severe ICA/CCA stenosis, radiomic features (volume: AUC=0.94, 95% CI 0.88-0.96; section: AUC=0.92, 95% CI 0.86-0.93) were significantly better than spectral segmentation features (volume: AUC = 0.86, 95% CI 0.74-0.87; section: AUC = 0.68, 95% CI 0.66-0.78) (P < .001). Spectral radiomic features predicted revascularization procedure (AUC = 0.77) and the presence of ipsilateral intra- cranial ischemic changes (AUC = 0.76).

CONCLUSION

Spectral segmentation and radiomic features from DECTA can differentiate patients with differ- ent luminal ICA/CCA stenosis grades.