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  4. Detection of calcified plaques: comparison between coronary CT angiography and thin-slice non-contrast CT with deep learning-aided image registration
 
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2026
Journal Article
Title

Detection of calcified plaques: comparison between coronary CT angiography and thin-slice non-contrast CT with deep learning-aided image registration

Abstract
Objectives: To investigate whether coronary CT angiography (CCTA) misses calcified plaques detected by thin-slice non-contrast CT (NCCT). Materials and methods: This study included patients from two sites in the DISCHARGE trial for whom both 0.5 mm thin-slice NCCT and CCTA were available. Plaques on CCTA were defined as missed if they showed no spatial overlap with NCCT-detected plaques after deep learning-aided co-registration. Comparisons of plaque volume, density, and local coronary luminal attenuation between plaques missed and those detected by CCTA were performed using the Mann–Whitney U-test. In addition, the presence of these plaques on standard calcium scoring CT was assessed. Interobserver agreement was assessed using the intraclass correlation coefficient and Bland‒Altman analysis. Results: This study included 45 patients (40% female, mean age 62 ± 11 years), in whom CCTA missed 37.6% of calcified plaques detected by NCCT (121/322). Missing calcified plaques on CCTA misclassified 8.9% of patients (4/45) as having no plaques. Compared with detected plaques, plaques missed by CCTA were both significantly smaller in volume (3.0 mm³ [IQR, 1.5–4.9] vs. 9.2 mm³ [IQR, 4.3–21.9], p < 0.001) and had lower density (212.7 HU [IQR, 174.5–242.4] vs. 292.7 HU [IQR, 243.2–361.3], p < 0.001). Only 44.0% of plaques (53/121) missed by CCTA were detected by standard calcium scoring CT. Interobserver analysis demonstrated excellent agreement for calcified plaque volume on CCTA (ICC = 0.91) and NCCT (ICC = 0.98). Conclusion: CCTA missed more than one-third of coronary calcified plaques that are identifiable on co-registered thin-slice NCCT, which suggests an underutilized role of thin-slice NCCT in clinical practice.
Author(s)
Schulze, Kenrick
Charité – Universitätsmedizin Berlin
Biavati, Federico
Charité – Universitätsmedizin Berlin
Föllmer, Bernhard
Charité – Universitätsmedizin Berlin
Tsogias, Sotirios
Charité – Universitätsmedizin Berlin
Chimed, Surenjav
Mongolian National University of Medical Sciences
Balogh, Hanna
Charité – Universitätsmedizin Berlin
Nagy, Norbert D.
Charité – Universitätsmedizin Berlin
Yavuz, Ferhat
Charité – Universitätsmedizin Berlin
Stantien, Anne Marieke
Charité – Universitätsmedizin Berlin
Abdelrahman, Renad Heyam
Charité – Universitätsmedizin Berlin
Lukas, Steffen
Charité – Universitätsmedizin Berlin
Bosserdt, Maria
Charité – Universitätsmedizin Berlin
Kachelrieß, Marc
German Cancer Research Center
Samek, Wojciech  
Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut HHI  
Dewey, Marc
Charité – Universitätsmedizin Berlin
Journal
European radiology  
Open Access
File(s)
Download (1.54 MB)
Rights
CC BY 4.0: Creative Commons Attribution
DOI
10.1007/s00330-026-12501-y
10.24406/publica-8578
Additional link
Full text
Language
English
Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut HHI  
Keyword(s)
  • Artificial intelligence

  • Coronary artery calcification

  • Coronary computed tomography angiography

  • Image registration

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