Proposal of simplified CT syndesmophyte score (sCTSS) and comparison with CTSS in patients with ankylosing spondylitis.
Hong-Ki MinSe Hee KimSang-Heon LeeHae-Rim KimPublished in: Scientific reports (2023)
The CT syndesmophyte score (CTSS) can evaluate spinal progression more precisely than mSASSS in ankylosing spondylitis (AS); however, it is complex and time consuming. Here, we propose a simplified CTSS (sCTSS) for measuring spinal structural changes in AS. Patients with AS were recruited from a single tertiary hospital. Baseline and 2-year follow-up whole spine CT images were used to calculate CTSS and sCTSS. The sCTSS used the anterior and posterior vertebral corners, and ranged 0-184. Intraclass correlation coefficients (ICC) were calculated, as well as the smallest detectable changes. Fifty AS patients were included. For reader 1, the mean sCTSS at baseline and 2-year follow-up were 11.7 ± 14.6 and 15.8 ± 16.1, whereas those for reader 2 were 12.0 ± 12.5 and 15.8 ± 15.7, respectively. The ICCs for CTSS at baseline and at 2-year follow-up were 0.97 (95% confidence interval [CI] 0.96-0.99) and 0.98 (0.97-0.99), respectively, and that for changes over the 2 years was 0.48 (95% CI 0.23-0.67). For sCTSS, the ICCs were 0.96 (95% CI 0.92-0.97), 0.97 (95% CI 0.94-0.98), and 0.58 (95% CI 0.36-0.74), respectively. Detection rates for syndesmophyte progression were comparable between CTSS and sCTSS. The detection rate for syndesmophytes on only lateral side was 13.2 and 11.4%, and 11.4 and 15.2% at baseline and 2-year follow-up (reader 1 and 2). sCTSS and CTSS showed similar detection rates for syndesmophyte progression. sCTSS may be a reliable method for evaluating spinal structural damage in AS.
Keyphrases
- ankylosing spondylitis
- spinal cord
- rheumatoid arthritis
- computed tomography
- disease activity
- image quality
- contrast enhanced
- dual energy
- end stage renal disease
- real time pcr
- label free
- ejection fraction
- chronic kidney disease
- newly diagnosed
- oxidative stress
- magnetic resonance imaging
- peritoneal dialysis
- systemic lupus erythematosus
- deep learning
- machine learning