A Novel Machine Learning-Based Semi-Automated Phantom-Less QCT Model for Osteoporosis Screening on 100 kVp Ultra-Low-Dose Chest CT: A Phantom Study.

RATIONALE AND OBJECTIVES: Opportunistic osteoporosis screening using chest CT is increasingly explored, yet conventional QCT models are calibrated at 120 kVp and may be inaccurate for ultra-low-dose scans acquired at lower tube voltages. This study aimed to develop and validate a machine learning-assisted phantom-less QCT (PL-QCT) model for BMD quantification at 100 kVp and assess its diagnostic performance. METHODS: Sixteen repeated European Spine Phantom (ESP) scans were acquired using a 100 kVp ultra-low-dose chest CT protocol. A total of 508 patients were retrospectively included for model training and internal validation. A 100 kVp PL-QCT model was developed, calibrated against ESP reference values, and compared with a conventional 120 kVp QCT model. External validation was performed on an independent CT system using ESP scans and 197 patients under a 100 kVp chest CT protocol. Diagnostic performance was evaluated in 178 individuals with both DXA and chest CT. RESULTS: The mean effective dose was 0.82±0.19 mSv. In internal validation, the 100 kVp model showed significantly lower BMD error than the 120 kVp model (2.39±7.12 vs 16.68±8.26 mg/cm³, p<0.0001), with improved accuracy across L1-L3. External validation confirmed lower error for the 100 kVp model (-0.11±4.39 vs 13.78±5.10 mg/cm³). Agreement with DXA was 88.8%. CONCLUSION: The 100 kVp machine learning-assisted PL-QCT model enables accurate BMD quantification from ultra-low-dose chest CT, outperforming conventional 120 kVp models and supporting reliable cross-scanner opportunistic osteoporosis screening.