Imperial College London Featured PhD Programmes
Gdansk University of Technology Featured PhD Programmes
University of Reading Featured PhD Programmes

A novel validated subject-specific finite element model to predict the risk of fracture in patients with vertebral metastases


   Department of Oncology and Metabolism

This project is no longer listed on FindAPhD.com and may not be available.

Click here to search FindAPhD.com for PhD studentship opportunities
  Dr Enrico Dall'Ara, Prof D Lacroix, Prof Janet Brown  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

The management of patients with vertebral metastases is critical, due to the challenging assessment of vertebral stability and the need to decide whether the patient needs an invasive intervention to fix the spine. The best clinical tool available to date is the Spine Instability Neoplastic Score (SINS), a scoring system that accounts for qualitative information assessed by the surgeon such as pain, type of lesion, and location of the lesion in the spine. This tool is far from optimal and in several cases it is inconclusive or it leads to overtreatment of patients. The finite element (FE) method applied to computed tomography (CT) images of the patient, has been used to estimate the femoral and vertebral strength in osteoporotic patients. Nevertheless, the application of this method to assess metastatic spines is still limited, due to modelling challenges and the lack of proper validation of the models against accurate measurements in the laboratory, step required before its application in clinical studies. In a previous study we have confirmed the feasibility of the application of the FE method to study the stability of vertebra with metastases under simple compressive loads. However, the lack of experimental validation of the FE outputs for the complex metastatic vertebral structure has limited its clinical application. The lack of validation is due to the experimental challenges in characterizing the structural and local mechanical properties of metastatic vertebrae, as standard biomechanical tests cannot be used in this case due to the heterogeneity of the internal structure of the affected bones. Moreover, there are computational challenges in developing accurate and efficient computational models based on medical images. In the past two years we have developed a unique experimental pipeline for the biomechanical assessment of the metastatic vertebra under complex loading conditions by combining in situ mechanical testing of spine segments within a micro-computed tomography and high-resolution imaging. The acquired images of the vertebra in its unloaded and loaded configurations can be used to track the heterogeneous deformation of the tissue under complex loading, by using digital volume correlation (DVC).

In this study we will process the images acquired during the biomechanical testing with the BoneDVC, the most precise global DVC approach for bone applications developed in our group, and use this unique experimental dataset for validating the outputs of the computational models. We will develop a novel pipeline to create subject-specific FE models of the human vertebrae with or without metastatic lesions which outputs will be validated against the experimental measurements. The experimental database will allow us to identify the most accurate modelling approach to use in order to predict the mechanical properties of the metastatic vertebra, and to evaluate its risk of fracture in different loading conditions.

Entry Requirements:

Candidates must have a first or upper second class honors degree. Experience with Finite Element modelling of biological tissues will be beneficial.

How to apply:

Please complete a University Postgraduate Research Application form available here: www.shef.ac.uk/postgraduate/research/apply

Please clearly state the prospective main supervisor in the respective box and select 'Oncology & Metabolism' as the department. 

Enquiries:

Interested candidates should in the first instance contact Dr Enrico Dall'ara - [Email Address Removed]


Funding Notes

This opportunity is for self funded candidates.
Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.

PhD saved successfully
View saved PhDs