The spine is the most common location of metastatic disease in the skeleton. The occurrence of bone metastasis can lead to severe clinical consequences and a significant decline in quality of life. The evaluation of metastatic disease in the spine has to date been mainly qualitative. More widespread access to multiple imaging modalities has motivated the development of 3D methods to quantitatively evaluate metastatic disease in the spine. Quantitative evaluation is important both in assessing stability of the metastatic spine and the progression/ response of the tumour and bone to treatment over time. Previous studies quantifying stability in the metastatic spine have focused primarily on osteolytic tumours. Local and systemic treatments have impacted the nature of vertebral metastasis, increasing the occurrence of mixed osteolytic and osteoblastic disease. Thus, it is important to focus analyses on models able to accurately represent diverse distribution patterns found in bony metastasis. Preclinical models are widely used in studying the process of metastasis and are able to represent both osteolytic and osteoblastic disease. This proposal aims to establish the biomechanical implications of metastatic disease in the spine through the evaluation of stability and tumour burden in a preclinical model using a multifaceted engineering-based approach. It is hypothesized that the use of automated analysis techniques applied to multimodality imaging will allow quantification of the impact of metastasis on biomechanical stability, tumour burden and bony architecture in the spine, and motivate prediction models that accurately reflect vertebral integrity in both osteolytic and mixed osteolytic/osteoblastic models of spinal metastasis. Specifically, this work aims to:​ 1) Utilize and compare µMR and µCT based radiologic methods to quantify tumour involvement and vertebral architecture in a rat model of spinal metastasis;​ and 2) Evaluate the ability of 2D, 3D, and continuum based methods to quantify structural integrity in vertebral metastasis. Overall, this work will focus on developing automated methods to quantify stereologic parameters, and quality in the metastatic spine and the evaluation of stability measures from 2D structural rigidity, Finite Element analysis, image registration and experimental methods. Ultimately this work will yield automated analysis techniques and evaluate the abilities of these methods to predict failure in metastatic vertebrae.