Experimental validation of a voxel-based finite element model simulating femoroplasty of lytic lesions in the proximal femur

Achieving the ideal balance between biological and mechanical requirements in composite bone scaffolds through a voxel-based approach

Achieving successful bone regeneration necessitates the design of scaffolds that meet diverse biological and mechanical requirements, often leading to conflicts in the design parameters. A key conflict arises between scaffold porosity and stiffness. Increasing porosity facilitates cell infiltration and nutrient exchange, promoting bone regeneration. However, higher porosity compromises scaffold stiffness, which is crucial for providing structural support in the defective region. Furthermore, appropriate scaffold stiffness is crucial for preventing stress shielding. Conventional geometry-based design methods utilizing single-phase materials have limited flexibility in resolving such conflicts. To address this challenge, we propose a voxel-based method for designing composite scaffolds composed of hydroxyapatite (HA) and polylactic acid (PLA). Our strategy involves first satisfying primary biological requirements by selecting appropriate porosity, pore shape, and size. Subsequently, scaffold stiffness requirements are met by selecting suitable phase materials and tuning their contents. The study demonstrates that the voxel-based approach effectively balances both biological and mechanical requirements in scaffold design. This method addresses the limitations of traditional designs by achieving an optimal balance between porosity and stiffness, which is crucial for scaffold performance in biomedical applications. Moreover, the scaffolds designed using this method can be manufactured using voxel-based 3D printing technology, which is emerging in the field. Future advancements in voxel-based 3D printing technology will further enhance the feasibility and practicality of this approach for bone tissue engineering applications.

The effect of defect size and location on the fracture risk of proximal tibia, following tumor curettage and cementation: An in-silico investigation

Even though, proximal tibia is a common site of giant cell tumor and bone fractures, following tumor removal, nonetheless very little attention has been paid to affecting factors on the fracture risk. Here, nonlinear voxel-based finite element models based on computed tomography images were developed to predict bone fracture load with defects with different sizes, which were located in the medial, lateral, anterior, and posterior region of the proximal tibia. Critical defect size was identified using One-sample t-test to assess if the mean difference between the bone strength for a defect size was significantly different from the intact bone strength. Then, the defects larger than critical size were reconstructed with cement and the mechanics of the bone-cement interface (BCI) was investigated to find the regions prone to separation at BCI. A significant increase in fracture risk was observed for the defects larger than 20 mm, which were located in the medial, lateral and anterior regions, and defects larger than 25 mm for those located in the posterior region of the proximal tibia. Furthermore, it was found that the highest and lowest fracture risks were associated with defects located in the medial and posterior regions, respectively, highlighting the importance of selecting the initial location of a cortical window for tumor removal by the surgeon. The results of the BCI analysis showed that the location and size of the cement had a direct impact on the extent of damage and its distribution. Identification of critical regions susceptible to separation at BCI, can provide critical comments to surgeons in selecting the optimal cement augmentation technique, which may ultimately prevent unnecessary surgical intervention, such as using screws and pins.

Mechanical Gains Associated With Virtual Prophylactic Intramedullary Nail Fixation in Femurs With Metastatic Disease

Background

Many patients with metastatic bone disease (MBD) of the femur undergo prophylactic surgical fixation for impending pathologic fractures; intramedullary nailing (IMN) being the most common fixation type. However, surgeons often question if IMN fixation provides sufficient improvements in mechanical strength for particular metastatic lesions. Our goal was to use patient-specific finite element (FE) modeling to computationally evaluate the effects of simulated IMN fixation on the mechanics of femurs affected with MBD.

Methods

Computed tomography (CT) scans were available retrospectively from 48 patients (54 femurs) with proximal femoral metastases. The CT scans were used to create patient-specific, non-linear, voxel-based FE models of the femur, simulating the instant of peak hip joint contact force during normal walking. FE analyses were repeated after incorporating virtual IMN fixation (Smith and Nephew, TRIGEN INTERTAN) into the same femurs. Femur strength and load-to-strength ratio (LSR; lower LSR indicates lower fracture risk) were compared between untreated and IMN conditions using statistical analyses.

Results

IMN fixation resulted in a very modest average 10% increase in mechanical strength (p<0.001), which was associated with a slight 7% reduction in fracture risk (p<0.001). However, there was considerable variation in fracture risk reduction between individual femurs (0.13-50%). In femurs with the largest reduction in fracture risk (>10%), IMN hardware directly passed through a considerable section of that femur's metastatic lesion. Femurs with lytic (10%) and diffuse (9%) metastases tended to have greater reductions in fracture risk compared to femurs with blastic (5%) and mixed (4%) metastases (p=0.073).

Conclusion

Given the mechanically strong baseline condition of most femurs in this cohort, evident by the low fracture risk at the time of CT scanning, the relative increase in stiffness with the addition of the IMN hardware may not make a substantial contribution to overall mechanical strength. The mechanical gains of IMN fixation in femurs with MBD appear most beneficial when the hardware traverses an adequate section of the lesion. Level of Evidence: III.

The predictive ability of a QCT-FE model of the proximal femoral stiffness under multiple load cases is strongly influenced by experimental uncertainties

Despite significant improvements in terms of the predictive ability of Quantitative Computed Tomography based Finite Element (QCT-FE) models in estimating femoral strength (fracture load and stiffness), no substantial clinical adoption of this method has taken place to date. Narrowing the wide variability of FE results by standardizing the methodology and validation protocols, as well as reducing the uncertainties in the FEA process have been proposed as routes towards improved reliability. The aim of this study was to: First, validate a QCT-FE model of proximal femoral stiffness in multiple stance load cases, and second, using a parametric approach, determine the influence of select experimental and modeling parameters on the predictive ability of our model. Ten fresh frozen human femoral samples were tested in neutral stance, 15° adducted and 15° abducted load cases. Voxel-based linear-elastic QCT-FE models of the samples were generated to predict the models' stiffness values in all load cases. The base FE models were validated against the experimental results using linear regression. Thirty six deviated models were created using the minimum and maximum values of experiment-based "plausible range" for 18 parameters in 4 categories of embedding, loading, material, and segmentation. The predictive ability of the models were compared in terms of the coefficient of determination (R²) of the linear regression between the measured and predicted stiffness values in all load cases. Our model was capable of capturing 90% of the variation in the experimental stiffness of the samples in neutral stance position (R² = 0.9, concordance correlation coefficient (CCC) = 0.93, percent root mean squared error (RMSE%) = 8.4%, slope and intercept not significantly different from unity and zero, respectively). Embedding and loading categories strongly affected the predictive ability of the models with an average percent difference in R² of 4.36% ± 2.77 and 2.96% ± 1.69 for the stance-neutral load case, respectively. The performance of the models were significantly different in adducted and abducted load cases with their R² dropping to 71% and 70%, respectively. Similarly, off-axes load cases were affected by the parameters differently compared to the neutral load case, with the loading parameter category imposing more than 10% difference on their R², larger than all other categories. We also showed that automatically selecting the best performing plausible value for each parameter and each sample would result in a perfectly linear correlation (R²> 0.99) between the "tuned" model's predicted stiffness and experimental results. Based on our results, high sensitivity of the model performance to experimental parameters requires extra diligence in modeling the embedding geometry and the loading angles since these sources of uncertainty could dwarf the effects of material modeling and image processing parameters. The results of this study could help in improving the robustness of the QCT-FE models of proximal femur by limiting the uncertainties in the experimental and modeling steps.