Fereydoonpour, Mehran

Vertebral fractures are among the most common skeletal injuries and present significant clinical and biomechanical challenges, particularly in older adults and individuals with low bone density. Accurate prediction of vertebral mechanical response and failure under varying loading conditions is essential for improving understanding of spinal injury mechanisms. This study develops a density-dependent viscoelastic analytical model to predict the stiffness and fracture force of human vertebral specimens subjected to different compression rates. The vertebral body is represented as a composite structure consisting of a cortical shell and a trabecular core. Cortical bone is modeled as a linear elastic material, whereas trabecular bone is described using a Kelvin–Voigt viscoelastic formulation. Density-dependent constitutive relationships are incorporated for the elastic modulus and viscous coefficient of trabecular bone. Unknown material parameters are identified through optimization using the Nelder–Mead algorithm, based on experimental compression data from cadaveric vertebral specimens tested under quasi-static and dynamic loading conditions. The calibrated model reproduced the overall trend of specimen-to-specimen mechanical variation observed experimentally. Predicted stiffness values were in reasonable agreement with measured data. Fracture force predictions showed moderate agreement for dynamically tested specimens (R2 = 0.60), which improved to R2 = 0.88 after exclusion of one statistically identified outlier. Compared with a purely linear elastic formulation, the proposed viscoelastic model demonstrated modest improvement in stiffness prediction and more substantial improvement in fracture force prediction. These findings indicate that incorporating density-dependent viscoelastic effects improves representation of vertebral mechanical behavior, particularly at higher loading rates. Owing to its simplicity and computational efficiency, the proposed model requires only limited imaging input and may be useful for future biomechanical investigations, rapid screening, and injury risk prediction.

ABSTRACT Accurately predicting vertebral fracture risk in metastatic spines remains a critical challenge in clinical practice. This study developed and validated a QCT‐based finite element analysis (QCT/FEA) approach to investigate the combined effects of baseline bone strength and tumor size on vertebral structural integrity. Areal bone mineral density (aBMD) was also calculated from QCT data to evaluate the reduction in bone density with increasing defect size. Nine cadaveric vertebral bodies were analyzed under varying tumor sizes (0%, 20%, 35%, and 50%). The results demonstrated a strong correlation between experimentally measured and computationally predicted failure forces ( r = 0.97, p < 0.001) and aBMD values ( r = 0.96, p < 0.001). Vertebral strength decreased linearly with increasing tumor size. Importantly, the study revealed that baseline vertebral strength plays a crucial role in fracture risk assessment, often surpassing the impact of tumor size alone. Tumor size reduced vertebral strength at a rate 84% faster than bone density ( p = 0.009), highlighting a greater impact of tumor defects on bone fracture force than on bone density. These findings suggest that relying solely on tumor size for fracture risk prediction may be insufficient. Incorporating baseline bone strength into predictive models significantly enhances accuracy and reliability, providing valuable insights for clinical decision‐making and personalized treatment strategies. This study underscores the importance of advanced computational tools in improving vertebral fracture risk assessment in metastatic spine cases.