Simulated lesions representative of metastatic disease predict proximal femur failure strength more accurately than idealized lesions. (9th June 2020)

• Record Type: Journal Article
• Authors: Johnson, Joshua E.; Brouillette, Marc J.; Permeswaran, Palani T.; Miller, Benjamin J.; Goetz, Jessica E.
• Is Part Of: Journal of biomechanics. Volume 106(2020)
• Publication Date: 2020-06-09
• Subjects: Amorphous Lesion -- Offset Torsion -- Linear Finite Element Model -- Nonlinear Finite Element Model -- Voxel Mesh; Animal mechanics -- Periodicals; Biomechanics -- Periodicals; Biomechanics -- Periodicals; Mécanique animale -- Périodiques; Biomécanique -- Périodiques; Electronic journals; 571.4305
• DOI: 10.1016/j.jbiomech.2020.109825
• Languages: English
• ISSNs: 0021-9290

Abstract

Abstract: Metastatic disease in bone is characterized by highly amorphous and variable lesion geometry, with increased fracture risk. Assumptions of idealized lesion geometry made in previous finite element (FE) studies of metastatic disease in the proximal femur may not sufficiently capture effects of local stress/strain concentrations on predicted failure strength. The goal of this study was to develop and validate a FE failure model of the proximal femur incorporating artificial defects representative of physiologic metastatic disease. Data from 11 cadaveric femur specimens were randomly divided into either a training set (n = 5) or a test set (n = 6). Clinically representative artificial defects were created, and the femurs were loaded to failure under offset torsion. Voxel-based FE models replicating the experimental setup were created from the training set pre-fracture computed tomography data. Failure loads from the linear model with maximum principal strain failure criterion correlated best with the experimental data (R 2 = 0.86, p = 0.024). The developed model was found to be reliable when applied to the test dataset with a relatively low RMSE of 46.9 N, mean absolute percent error of 12.7 ± 17.1%, and cross-validation R 2 = 0.88 (p < 0.001). Models simulating realistic lesion geometry explained an additional 26% of the variance in experimental failure load compared to idealized lesion models (R 2 = 0.62, p = 0.062). Our validated automated FE model representative of physiologic metastatic disease may improve clinical fracture risk prediction and facilitate research studies of fracture risk during functional activities and with treatment interventions.