Reproducibility of automated opportunistic CT-based volumetric bone mineral density measurements at the thoracic spine

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Objectives: Opportunistic CT-based screening for osteoporosis has been shown to be cost-effective and may help to reduce the existing treatment gap in patients. Reproducibility has not yet been investigated at thoracic levels, which is becoming increasingly relevant with upcoming lung cancer screening programs. Methods: Scans from 178 patients with 2 consecutive CT examinations within 1 month were retrospectively included. In all available reconstructions, automated extraction of trabecular volumetric bone mineral density (vBMD) was performed, including HU-to-BMD conversion and contrast phase correction. Mean trabecular vBMD was calculated for 2 thoracic regions (T5-T7 and T8-T10). Reproducibility was calculated as the root mean square coefficient of variation (RMSCV) under 6 reproducibility scenarios, ranging from different reconstructions of the same scan to measurements across different scanners and contrast phases. Results: In total, 914 vBMD observations were analyzed. RMSCV values ranged from 0.83% to 4.90% for T5-T7 and from 1.4% to 6.8% for T8-T10. One-way ANOVA showed a significant overall difference between reproducibility scenarios in both vertebral regions (all P < .001), with post hoc analyses demonstrating a stepwise increase in RMSCV with technical complexity. Conclusions: Our results support the clinical applicability of opportunistic osteoporosis screening at the thoracic spine across diverse CT acquisition protocols. With the upcoming national lung cancer screening programs utilizing low-dose CT (LDCT), an extraordinary opportunity emerges to address the substantial osteoporosis treatment gap through opportunistic bone health assessment. Advances in knowledge: This work is novel in extending reproducibility validation for opportunistic osteoporosis screening from the lumbar spine to thoracic vertebrae.
Funding Information
  • Bayerische Forschungsstiftung (AZ-1634-24)

This publication has 31 references indexed in Scilit: