The negative consequences of fracture in the metastatic spine motivates improved bone quality assessment and fracture risk prediction. This study aims to automate preclinical microcomputed tomography (µCT) image identification of osteoblastic metastatic disease in vertebrae, characterize how metastases affect the material/mechanical properties of bone tissue and extend finite element (FE) modeling to include pathological changes to evaluate damage and fracture behaviour. A combination of image analysis techniques (µCT-based stereological analysis, back scatter electron microscopy) and computational modeling (voxel-based µFE modeling of postyield mechanical behaviour experimentally validated through sequentially imaged mechanical testing) is used to characterize the structural and material level impact of osteoblastic disease in vertebral trabecular bone. Methods for automated segmentation of osteoblastic lesions are also developed with µCT feature extraction and random forest classification methods to enable identification of pathologic tissue. Advanced preclinical understanding of osteoblastic disease can provide a foundation for improved guidelines for clinical treatment decision-making