A new approach of metastatic bone fracture prediction using a patient-specific model including metastatic tissue, daily-life activities and local failure criteria

Project Information

METABONE

Grant agreement ID: 895139

DOI

10.3030/895139

Project closed

EC signature date 27 February 2020

Start date 15 June 2020

End date 14 June 2022

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost

€ 184 707,84

EU contribution

€ 184 707,84

Coordinated by

UNIVERSITE LYON 1 CLAUDE BERNARD
France

Periodic Reporting for period 1 - METABONE (A new approach of metastatic bone fracture prediction using a patient-specific model including metastatic tissue, daily-life activities and local failure criteria)

Reporting period: 2020-06-15 to 2022-06-14

Summary of the context and overall objectives of the project

Osteolytic bone metastases are responsible for long bone fracture leading to restricted mobility, surgery, or medullar compression that severely alter quality of life and have a huge socio-economic impact. Current fragility scores to estimate the fracture risk in patients with metastatic femur are based on qualitative evaluation from Quantitative Computed Tomography (QCT) scans and lack sensitivity and specificity. Efforts are now made towards the development of patient-specific finite element models to assess the strength of tumoral bone segments, but their accuracy is hampered by several limitations, including a limited knowledge of metastatic bone mechanical properties, simulations performed only for single stance loading condition, and simulations providing a global failure criteria. The aim of METABONE is therefore to use a novel approach to better predict the fracture risk of metastatic femur. A patient-specific finite element model will be developed based on QCT scans, which will include the real material properties of ex vivo human metastatic bone determined experimentally on the first part of this project. The composition and mechanical behaviour of diseased bone tissue are hypothesised to be rather different from healthy tissue and influence bone strength femoral strength. This model will be used clinically on patients with osteolytic lesions located in proximal femur to assess the fracture risk during daily life activities, using a local failure criteria and a range of different loading conditions. This novel methodology, combining experimental and numerical approaches, is expected to significantly improve the accuracy of fracture risk prediction. A successful completion of METABONE will have the potential to guide clinical decision making, by providing clinicians with a more accurate tool to optimize locomotor strategy and oncology program, in order to prevent bone fracture, improve survival and quality of life of the patients.
During this fellowship, the mechanical properties of primary tumour and bone metastases have been characterised and implemented into a subject-specific finite element model of metastatic femur. A quantification of several sources of uncertainties in the model and how they influence the predicted failure load have been performed and will allow a standardisation of the whole process for a clinical application.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

During this project, I characterised the elastic and viscoelastic properties of bone metastasis and primary tumour (breast, kidney, and thyroid) tissue and I correlated them with tissue structure and composition. I showed that tumour mechanical properties were differentially affected depending on organ and histological type, and that composition changes between normal and metastatic areas depend on the type of lesion (lytic, mixed, blastic) and origin of primary cancer. From a clinical perspective, our findings on the mechanical properties of tumour tissue have the potential to significantly advance the field of cancer research. They have implications for diagnosis purposes and form a first basis to understand how mechanical alterations are linked to aggressiveness and metastatic potential of cancer.
In parallel, I worked on the development of a subject-specific finite element model of metastatic femurs, using ex vivo data (QCT scans and biomechanical tests). I conducted a sensitivity analysis to evaluate the influence of bone metastasis mechanical properties on the predicted failure load of the model, depending on defect size and location. I showed that tumour mechanical properties do not have a significant influence on the failure load for a small defect, but result in a variation of the predicted failure load when the tumour size increases, demonstrating the necessity to take into account the variability in tumour mechanical properties for failure load prediction. The method that I implemented for changing the mechanical properties of the tumour and the size of the tumour have clinical implications, as it can allow the follow-up of patients and predict for which tumour size there is a risk of fracture. We also evaluated the inter-operator variability of the finite element model and we identified which parameters have the highest influence on the failure load prediction. These findings demonstrate the need to standardize the whole process for a clinical application. They will be used to automate the method and quantify the uncertainty on the predicted failure load.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

I characterised ex vivo human bone metastases and primary tumours. I developed a dedicated protocol for the characterisation of the elastic and viscoelastic properties of primary tumour tissue and metastatic bone using Atomic Force Microscopy (AFM), which allows the characterisation of materials with various stiffness. This study was the first one to compare the mechanical properties of different types of tumour and to characterise their viscoelastic properties. I showed that tumour mechanical properties were differentially affected depending on organ and histological type. These findings suggest a unique mechanical profile associated with each subtype of cancer. Therefore, viscosity could be a discriminator between tumour and normal thyroid tissue, while elasticity could be a discriminator between the subtypes of breast, kidney and thyroid cancers. From a clinical perspective, our findings on the mechanical properties of tumour tissue have the potential to significantly advance the field of cancer research. They have implications for diagnosis purposes and form a first basis to understand how mechanical alterations are linked to aggressiveness and metastatic potential of cancer.
In parallel, I worked on the development of a subject-specific finite element model of metastatic femur to predict its fracture risk. Several uncertainties exist on the whole method and the aim of this numerical part was to quantify and reduce some of these uncertainties. To identify and quantify the sources of uncertainty in the numerical framework, I (a) assessed the inter-operator variability in the finite element model, and (b) performed a sensitivity study on the mechanical properties of the tumour. The inter-operator study demonstrated that specific guidelines are mandatory for the finite element analysis. More specifically, there is a need to (a) control and estimate the uncertainty on these operator-dependent parameters in the FE analysis, and (b) standardize the whole process for a clinical application. the sensitivity analysis on tumour mechanical properties showed that a modification of tumour mechanical properties result in a variation of the predicted failure load when the tumour size increases. This results demonstrate the necessity to take into account the variability in tumour mechanical properties for failure load prediction. Efforts towards the quantification and reduction of the uncertainities in the model will allow its clinical application, to predict the fracture risk of metastatic femur in patients. The objective is to provide a decision tool for clinicians (cancer treatment or preventive surgery). This tool is expected to significantly improve the quality of life of patients with bone metastases and decrease costs of unnecessary surgery.

Project overview and main findings