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Submitted on 16 Jan 2020
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Failure prediction of metastatic bone with osteolytic
lesion in mice
Benjamin Delpuech, Stéphane Nicolle, Cyril Confavreux, Lamia Bouazza,
Phillipe Clezardin, David Mitton, Hélène Follet
To cite this version:
Benjamin Delpuech, Stéphane Nicolle, Cyril Confavreux, Lamia Bouazza, Phillipe Clezardin, et al..
Failure prediction of metastatic bone with osteolytic lesion in mice. 25th Congress of the European
Society of Biomechanics, Jul 2019, VIENNE, Austria. pp.1. �hal-02441862�
25th Congress of the European Society of Biomechanics, July 7-10, 2019, Vienna, Austria
Figure 1: Correlation between experimental and simulated ultimate loads
Table 1: Axial simulation results for ult. load for each limb
FAILURE PREDICTION OF METASTATIC BONE WITH OSTEOLYTIC
LESION IN MICE
B. Delpuechab, S. Nicollea, C. Confavreuxbc, L. Bouazzab, P. Clezardinb, D. Mittona, and H. Folletb
a Univ Lyon, Université Claude Bernard Lyon 1, IFSTTAR, LBMC UMR_T9406, 69622 Lyon, France; b Univ Lyon, Université Claude Bernard Lyon 1, INSERM, LYOS UMR 1033, 69008 Lyon, France; c Rheumatology Department, CEMOS, Centre Hospitalier Lyon Sud, Civil Hospices of Lyon, Lyon, France
Introduction
Metastatic cancer affects the skeleton [1]. Bone metastases weaken bones, and physicians have to decide the emergency of a surgical intervention. Current tools do not allow an accurate prediction of metastatic bone failure. Past studies showed that patient-specific finite element analysis (FEA) could contribute to improve this diagnosis [2]. However, in those studies, limitations were underlined (e.g. boundary conditions and mechanical properties of tumoral tissues) [2]. Therefore, the goal of this study to use an animal model for a deeper understanding of the modelling key factors.
Methods
Eight BALB/c nude mice were intra-tibially injected in their right limb with tumor cells inducing lytic lesions. After sacrifice and µCT imaging (10µm resolution), the tibia was subjected to a compressive strength test. Distal part was embedded in resin and the proximal part was loaded through a mold to well distribute the force. Each tibia was subjected to a pre-cycling (30 cycles between -0.5N and -2N at 0.5Hz) immediately followed by a quasi-static test (displacement 0.03mm/s) until failure. Stiffness and ultimate load of each limb were derived from these tests.
In order to assess the FEA simulation, 3D models were created from µCT images. Heterogeneous FEA models were built, with a Young’s Modulus based on grey scale [3, 4]. Simulations were performed using linear elastic properties (ANSYS). Experimental stiffness was identified (modifying the orientation of the applied force) and numerical ultimate load (Ult. Load) was used as an output. Failure was assessed using a modified Pistoia’s criterion [5].
Analysis of the results was done in two ways: a “global” analysis where the entire bone (≈10 mm length once embedded) was used and a “local” analysis where only 3mm around the tumor were considered.
Results and Discussion
Ultimate load obtained from simulation was in good agreement with experimental data (r²=0.82 and slope close to 1, Figure 1). This result suggests that the failure criterion used is relevant.
Local and global analyses were then compared. Results (Table 1) showed two different behaviours. The first group shows larger differences between the global and local analyses (mean difference: 38%) while the second group shows a higher agreement (mean difference: 4%).
Interestingly, the first group corresponded to the limbs that experience either multiple failures or a low failure load, while the second had only one failure at higher loads.
Our results show that different failure mechanisms can be observed on mouse tibiae bearing tumors. This knowledge will be useful to better simulate bone fracture in case of tumors.
Further investigation will be conducted in order to find a link between lyses position, size and failure mechanism.
References
1. Coleman RE. Cancer.1588–94,1997. 2. Derikx et al. J of Biomechanics. 761-6, 2015 3. Keyak et al. Clin Orth Relat Res. 161-70. 2005 4. Yang et al. Bone.131-139, 2014
5. Nyman et al. BoneKey. 664, 2015
Acknowledgements
This work is supported by LabEx PRIMES (ANR-11-LABX-0063).
