RESUMO
Introduction: Pathologic vertebral fractures are devastating for patients with spinal metastases. However, the mechanical process underlying these fractures is poorly understood, limiting physician's ability to predict which vertebral bodies will fail. Method: Here, we show the development of a damage-based finite element framework producing highly reliable pathologic vertebral strength and stiffness predictions from X-Ray computed tomography (CT) data. We evaluated the performance of specimen-specific material calibration vs. global material calibration across osteosclerotic, osteolytic, and mixed lesion vertebrae that we derived using a machine learning approach. Results: The FE framework using global calibration strongly predicted the pathologic vertebrae stiffness (R 2 = 0.90, p < 0.0001) and strength (R 2 = 0.83, p = 0.0002) despite the remarkable variance in the pathologic bone structure and density. Specimen-specific calibration produced a near-perfect prediction of both stiffness and strength (R 2 = 0.99, p < 0.0001, for both), validating the FE approach. The FE damage-based simulations highlighted the differences in the pattern of spatial damage evolution between osteosclerotic and osteolytic vertebral bodies. Discussion: With failure, the FE simulation suggested a common damage evolution pathway progressing largely localized to the low bone modulus regions within the vertebral volume. Applying this FE approach may allow us to predict the onset and anatomical location of vertebral failure, which is critical for developing image-based diagnostics of impending pathologic vertebral fractures.