Deep Learning-Based Assessment of the Value of Vertebral Structural Parameters in Predicting Osteoporotic Vertebral Compression Fractures on Opportunistic CT Scans

Jian Qin,1,2 Jiang Li,1,2 Yanbo Liang,2 Mohan Yu,3 Zhangshen Ran,2 Qianqian Yao,2 Mohamad Noor Shafini,4 Izzad Ramli5 1Faculty of Health Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia; 2The Second Affiliated Hospital of Shandong First Medical University, Tai’an, Shandong, People’s Republic of China; 3Shandong First Medical University, Tai’an, Shandong, People’s Republic of China; 4Medical Imaging, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK; 5College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, Selangor, MalaysiaCorrespondence: Mohamad Noor Shafini, Medical Imaging, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK, Email [email protected] Izzad Ramli, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia, Email [email protected]: Osteoporotic vertebral compression fractures are common fragility fractures in older adults and are associated with substantial disability and healthcare burden. Opportunistic CT may provide a practical no-extra-radiation pathway for fracture-risk assessment, but quantitative vertebral structural parameters, especially cortical parameters, remain insufficiently studied.Purpose: To evaluate the value of vertebral structural parameters derived from opportunistic CT in identifying osteoporotic vertebral compression fractures and to develop a nomogram for individualized risk estimation.Methods: This retrospective single-center study included 298 patients aged 45 years or older who underwent chest or abdominal CT at the Second Affiliated Hospital of Shandong First Medical University between January 2020 and May 2024. Osteoporotic vertebral compression fracture status on sagittal CT was determined by two readers (one radiology resident and one senior physician) by consensus. A high-resolution 3D Dense-U-Net was used for automated vertebral segmentation and extraction of L1 structural parameters. Group comparisons were performed with t tests, one-way analysis of variance, and chi-squared tests as appropriate. Independent predictors were identified with univariate and multivariate logistic regression, and the nomogram was evaluated with receiver operating characteristic and calibration analyses.Results: Of the 298 participants (182 men and 116 women; mean age, 62.33 ± 9.56 years), 134 had osteoporotic vertebral compression fractures and 164 did not. In multivariate analysis, L1 cortical average area (OR, 0.99; 95% CI, 0.99– 1.00; P = 0.002) and L1 cortical average thickness (OR, 0.22; 95% CI, 0.13– 0.38; P < 0.001) were independent predictors. The nomogram achieved an area under the curve of 0.867 (95% CI, 0.817– 0.918) in the training cohort and 0.804 (95% CI, 0.709– 0.899) in the validation cohort.Conclusion: Quantitative vertebral structural parameters derived from opportunistic CT, particularly cortical parameters, showed good performance for identifying osteoporotic vertebral compression fracture risk in this single-center cohort. These findings support the potential value of deep learning-assisted quantitative assessment for opportunistic screening, although external validation is still required before broader clinical implementation.Keywords: osteoporotic vertebral compression fracture, opportunistic CT, cortical bone, deep learning-assisted segmentation, nomogram