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  Assessment of the risk of fracture in patients with spine metastases by patient-specific computational models based on Computed Tomography

   School of Medicine and Population Health

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  Dr Enrico Dall'Ara, Prof D Lacroix, Prof Janet Brown  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

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. In many cases the best clinical tool available to date is inconclusive or leads to overtreatment of patients. Finite Element computational models based on clinical images of the patient can accurately predict the vertebral fracture risk in osteoporotic patients but have not been optimised nor validated for metastatic vertebrae. This is due to computational modelling challenges and the lack of experimental assessment of the biomechanical properties of metastatic vertebrae that can be used to validate the outcomes of the models, a fundamental step before their clinical application.

Recently Dr Dall’Ara’s team has developed a unique experimental pipeline for the biomechanical assessment of the metastatic vertebra under complex loading conditions by combining mechanical testing and high-resolution imaging. This pipeline has been used to study the biomechanical properties of a large number of vertebrae with and without metastases. Therefore, for the first time we can compare the outputs of the computational models with experimental data, to understand how accurate are the models in predicting the mechanical properties of the vertebrae with lesions starting from the clinical images. Moreover, with this dataset we can identify the most accurate modelling approach to predict the biomechanical properties of vertebrae with metastatic lesions.

The hypothesis of this study is that subject-specific Finite Element models based on calibrated Computed Tomography images of the thoracolumbar spine can accurately predict the bone strength and fracture risk in oncology patients with vertebral metastases.

The objectives of the project are to:

1) Implement a semi-automatic pipeline to convert computed tomography (CT) images into non-linear subject-specific finite element (FE) models for the non-invasive prediction of mechanical properties of human metastatic vertebrae under complex loading scenarios;

2) Assess the ability of the models to predict the experimental data and optimise the modelling pipeline to identify the most accurate yet efficient modelling approach to be implemented in clinical settings;

3) Classify the vertebrae with metastases at high risk of fracture given CT images of oncology patients that have been followed up in a retrospective clinical dataset.

The student will learn how to create computational models based on medical images, validate the outputs of the models with state of the art biomechanical data, and apply the models to solve an important clinical problem.

Entry Requirements:

Candidates must have a first or upper second class honours degree or significant research experience. Experience in computational and/or experimental bone biomechanics will be beneficial.

How to apply:

Please complete a University Postgraduate Research Application form available here:

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


Interested candidates should in the first instance contact Dr Enrico Dall'Ara ([Email Address Removed]).

Proposed start date - October 2023

Engineering (12) Medicine (26)

Funding Notes

This scholarship funds Home fee and stipend at UKRI rate for 3.5 years. Overseas students may apply, but will have to fund the fee difference from elsewhere.

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