Predicting pathologic fractures in femora with metastatic lesions remains a clinical challenge. Currently used guidelines are inaccurate, especially to predict non-impeding fractures. This study evaluated the ability of a nonlinear quantitative computed tomography (QCT)-based homogenized voxel finite element (hvFE) model to predict patient-specific pathologic fractures. The hvFE model was generated highly automated from QCT images of human femora. The femora were previously loaded in a one-legged stance setup in order to assess stiffness, failure load, and fracture location. One femur of each pair was tested in its intact state, while the contralateral femur included a simulated lesion on either the superolateral- or the inferomedial femoral neck. The hvFE model predictions of the stiffness (0.47 < R
2
< 0.94), failure load (0.77 < R
2
< 0.98), and exact fracture location (68%) were in good agreement with the experimental data. However, the model underestimated the failure load by a factor of two. The hvFE models predicted significant differences in stiffness and failure load for femora with superolateral- and inferomedial lesions. In contrast, standard clinical guidelines predicted identical fracture risk for both lesion sites. This study showed that the subject-specific QCT-based hvFE model could predict the effect of metastatic lesions on the biomechanical behaviour of the proximal femur with moderate computational time and high level of automation and could support treatment strategy in patients with metastatic bone disease.
BackgroundThe distal radius is the most common osteoporotic fracture site occurring in postmenopausal women. Finite element (FE) modeling is a non-invasive mathematical technique that can estimate bone strength using inputted geometry/micro-architecture and tissue material properties from computed tomographic images. Our first objective was to define and compare in vivo precision errors for three high-resolution peripheral quantitative computed tomography (HR-pQCT, XtremeCT; Scanco) based FE models of the distal radius and tibia in postmenopausal women. Our second objective was to assess the role of scan interval, scan quality, and common region on precision errors of outcomes for each FE model.MethodsModels included: single-tissue model (STM), cortical-trabecular dual-tissue model (DTM), and one scaled model using imaged bone mineral density (E-BMD). Using HR-pQCT, we scanned the distal radius and tibia of 34 postmenopausal women (74 ± 7 years), at two time points. Primary outcomes included: tissue stiffness, apparent modulus, average von Mises stress, and failure load. Precision errors (root-mean-squared coefficient of variation, CV%RMS) were calculated. Multivariate ANOVA was used to compare the mean of individual CV% among the 3 HR-pQCT-based FE models. Spearman correlations were used to characterize the associations between precision errors of all FE model outcomes and scan/time interval, scan quality, and common region. Significance was accepted at P < 0.05.ResultsAt the distal radius, CV%RMS precision errors were <9 % (Range STM: 2.8–5.3 %; DTM: 2.9–5.4 %; E-BMD: 4.4–8.7 %). At the distal tibia, CV%RMS precision errors were <6 % (Range STM: 2.7–4.8 %; DTM: 2.9–3.8 %; E-BMD: 1.8–2.5 %). At the radius, Spearman correlations indicated associations between the common region and associated precision errors of the E-BMD-derived apparent modulus (ρ = −0.392; P < 0.001) and von Mises stress (ρ = −0.297; P = 0.007).ConclusionResults suggest that the STM and DTM are more precise for modeling apparent modulus, average von Mises stress, and failure load at the distal radius. Precision errors were comparable for all three models at the distal tibia. Results indicate that the noted differences in precision error at the distal radius were associated with the common scan region, illustrating the importance of participant repositioning within the cast and reference line placement in the scout view during the scanning process.
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