QCT-based finite element prediction of pathologic fractures in proximal femora with metastatic lesions

SSDL-an automated semi-supervised deep learning approach for patient-specific 3D reconstruction of proximal femur from QCT images

Deep Learning (DL) techniques have recently been used in medical image segmentation and the reconstruction of 3D anatomies of a human body. In this work, we propose a semi-supervised DL (SSDL) approach utilizing a CNN-based 3D U-Net model for femur segmentation from sparsely annotated quantitative computed tomography (QCT) slices. Specifically, QCT slices at the proximal end of the femur forming ball and socket joint with acetabulum were annotated for precise segmentation, where a segmenting binary mask was generated using a 3D U-Net model to segment the femur accurately. A total of 5474 QCT slices were considered for training among which 2316 slices were annotated. 3D femurs were further reconstructed from segmented slices employing polynomial spline interpolation. Both qualitative and quantitative performance of segmentation and 3D reconstruction were satisfactory with more than 90% accuracy achieved for all of the standard performance metrics considered. The spatial overlap index and reproducibility validation metric for segmentation-Dice Similarity Coefficient was 91.8% for unseen patients and 99.2% for validated patients. An average relative error of 12.02% and 10.75% for volume and surface area, respectively, were computed for 3D reconstructed femurs. The proposed approach demonstrates its effectiveness in accurately segmenting and reconstructing 3D femur from QCT slices.

Fracture risk assessment of vascularized medial femoral condylar bone graft: A finite element analysis

BACKGROUND

Vascularized medial femoral condyle (MFC) bone graft is useful for pseudarthrosis and osteonecrosis, but has the risk of fracture as a complication. This study aimed to create multiple three-dimensional (3D) finite element (FE) femur models to biomechanically evaluate the fracture risk in the donor site of a vascularized MFC bone graft.

METHODS

Computer tomography scans of the femurs of nine patients (four males and five females) with no left femur disease were enrolled in the study. A 3D FE model of the left femur was generated based on the CT images taken from the patients. The descending genicular artery (DGA), the main nutrient vessel in vascularized MFC bone grafts, divides into the proximal transversal branch (TB) and the distal longitudinal branch (LB) before entering the periosteum. Thirty-six different bone defect models with different sizes and locations of the harvested bone were created.

RESULTS

The highest stress was observed in the proximal medial and metaphyseal portions under axial and external rotation, respectively. In the bone defect model, the stress was most elevated in the extracted region's anterior or posterior superior part. Stress increased depending on proximal location and harvested bone size.

CONCLUSION

Increasing the size of the bone graft proximally raises the stress at the site of bone extraction. For bone grafting to non-load-bearing areas, bone grafting distally using LB can reduce fracture risk. If TB necessitates a larger proximal bone extraction, it is advisable to avoid postoperative rotational loads.

Patient-specific finite element modeling for fracture risk prediction in a canine model of osteosarcoma

Background

In cancer patients with bone tumors, pathological fractures are a major concern. Making treatment decision for these patients requires an evaluation of fracture risk, which is currently based on semi-qualitative criteria that lack patient-specificity. Because of this, there exists a need for quantitative fracture risk prediction tailored to the patient's individual bone geometry. To address this need, this study aims to develop and validate a finite element (FE) technique that can be used to create patient-specific models and more accurately identify fracture risk. Model validation was performed using canine radii.

Methods

Radii were harvested from eight canines euthanized for reasons unrelated to the study. A semicircular osteotomy was made in the distal portion of each bone to simulate tumor lysis. Samples underwent computed tomography (CT) scanning and were randomly assigned to loading groups for destructive mechanical testing. Three samples were tested in torsion, three in cantilever bending, and two in compression. FE models were created for each bone from the corresponding CT scan to replicate patient-specific geometry. Material properties were based on equations relating scan properties to elastic modulus. Boundary conditions and loads were added to the models based on the sample's treatment group. Stiffness and strain data were collected from both the mechanical testing and FE simulation, and yield load predictions were made based on maximum principal strain. Experimental and computational results were compared using a linear regression.

Results

The FE models were most accurate in predicting stiffness, followed by strain, with yield load having the lowest accuracy. Linear regressions resulted in R2 values of 0.9335 for bending and compression and 0.8798 for torsion.

Conclusions

The proposed FE technique is a valid method for predicting fracture in a canine model of osteosarcoma. This method could provide patient-specific, quantitative data to aid clinicians in decisions regarding surgical intervention for patients with bone tumors.

The influence of femoral lytic tumors segmentation on autonomous finite element analysis

BACKGROUND

The validated CT-based autonomous finite element system Simfini (Yosibash et al., 2020) is used in clinical practice to assist orthopedic oncologists in determining the risk of pathological femoral fractures due to metastatic tumors. The finite element models are created automatically from CT-scans, assigning to lytic tumors a relatively low stiffness as if these were a low-density bone tissue because the tumors could not be automatically identified.

METHODS

The newly developed automatic deep learning algorithm which segments lytic tumors in femurs, presented in (Rachmil et al., 2023), was integrated into Simfini. Finite element models of twenty femurs from ten CT-scans of patients with femoral lytic tumors were analyzed three times using: the original methodology without tumor segmentation, manual segmentation of the lytic tumors, and the new automatic segmentation deep learning algorithm to identify lytic tumors. The influence of explicitly incorporating tumors in the autonomous finite element analysis on computed principal strains is quantified. These serve as an indicator of femoral fracture and are therefore of clinical significance.

FINDINGS

Autonomous finite element models with segmented lytic tumors had generally larger strains in regions affected by the tumor. The deep learning and manual segmentation of tumors resulted in similar average principal strains in 19 regions out of the 23 regions within 15 femurs with lytic tumors. A high dice similarity score of the automatic deep learning tumor segmentation did not necessarily correspond to minor differences compared to manual segmentation.

INTERPRETATION

Automatic tumor segmentation by deep learning allows their incorporation into an autonomous finite element system, resulting generally in elevated averaged principal strains that may better predict pathological femoral fractures.

Evaluation of bone-related mechanical properties in female patients with long-term remission of Cushing's syndrome using quantitative computed tomography-based finite element analysis

BACKGROUND

Hypercortisolism in Cushing's syndrome (CS) is associated with bone loss, skeletal fragility, and altered bone quality. No studies evaluated bone geometric and strain-stress values in CS patients after remission thus far.

PATIENTS AND METHODS

Thirty-two women with CS in remission (mean age [±SD] 51 ± 11; body mass index [BMI], 27 ± 4 kg/m2; mean time of remission, 120 ± 90 months) and 32 age-, BMI-, and gonadal status-matched female controls. Quantitative computed tomography (QCT) was used to assess volumetric bone mineral density (vBMD) and buckling ratio, cross-sectional area, and average cortical thickness at the level of the proximal femur. Finite element (FE) models were generated from QCT to calculate strain and stress values (maximum principal strain [MPE], maximum strain energy density [SED], maximum Von Mises [VM], and maximum principal stress [MPS]). Areal BMD (aBMD) and trabecular bone score (TBS) were assessed by dual-energy X-ray absorptiometry (2D DXA).

RESULTS

Trabecular vBMD at total hip and trochanter were lower in CS as compared with controls (P < .05). Average cortical thickness was lower, and buckling ratio was greater in CS vs controls (P < .01). All strain and stress values were higher in CS patients vs controls (P < .05). 2D DXA-derived measures were similar between patients and controls (P > .05). Prior hypercortisolism predicted both VM (β .30, P = .014) and MPS (β .30, P = .015), after adjusting for age, BMI, menopause, delay to diagnosis, and duration of remission.

CONCLUSIONS

Women with prior hypercortisolism have reduced trabecular vBMD and impaired bone geometrical and mechanical properties, which may contribute to an elevated fracture risk despite long-term remission.

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.

Prescribing Exercise to Cancer Patients Suffering from Increased Bone Fracture Risk Due to Metastatic Bone Disease or Multiple Myeloma in Austria-An Inter- and Multidisciplinary Evaluation Measure

INTRODUCTION

In the current absence of specific functional fracture risk assessment technology, the planning of physical exercise interventions for cancer patients suffering from increased bone fracture risk remains a serious clinical challenge. Until a reliable, solely technical solution is available for the clinician, fracture risk assessment remains an inter- and multidisciplinary decision to be made by various medical experts. The aim of this short paper is depicting how this challenge should be approached in the clinical reality according to Austrian experts in cancer rehabilitation, presenting the best-practice model in Austria. Following referral from the specialist responsible for the primary cancer treatment (oncologist, surgeon, etc.), the physiatrist takes on the role of rehabilitation case manager for each individual patient. Fracture risk assessment is then undertaken by specialists in radiology, orthopedics, oncology, and radiation therapy, with the result that the affected bone regions are classified as being at highly/slightly/not increased fracture risk. Following internal clearance, exercise planning is undertaken by a specialist in exercise therapy together with the physiatrist based on the individual's fracture risk assessment. In the case in which the patient shows exercise limitations due to additional musculoskeletal impairments, adjuvant physical modalities such as physiotherapy should be prescribed to increase exercisability.

CONCLUSION

Exercise prescription for cancer patients suffering from increased fracture risk is an inter- and multidisciplinary team decision for each individual patient.

Fracture risk assessment and evaluation of femoroplasty in metastatic proximal femurs. An in vivo CT-based finite element study

The goal of this study was twofold. First, we aimed to evaluate the accuracy of a finite element (FE) model to predict bone fracture in cancer patients with proximal femoral bone metastases. Second, we evaluated whether femoroplasty could effectively reduce fracture risk. A total of 89 patients were included, with 101 proximal femurs affected with bone metastases. The accuracy of the model to predict fracture was evaluated by comparing the FE failure load, normalized for body weight, against the actual occurrence of fracture during a 6-month follow-up. Using a critical threshold, the model could identify whether femurs underwent fracture with a sensitivity of 92% and a specificity of 66%. A virtual treatment with femoroplasty was simulated in a subset of 34 out of the 101 femurs; only femurs with one or more well-defined lytic lesions were considered eligible for femoroplasty. We modeled their lesions, as well as the surrounding 4 mm of trabecular bone, to be augmented with bone cement. The simulation of femoroplasty increased the median failure load of the FE model by 57% for lesions located in the head/neck of the femur. At this lesion location, all high risk femurs that had fractured during follow-up effectively moved from a failure load below the critical threshold to a value above. For lesions located in the trochanteric region, no definite improvement in failure load was found. Although additional validation studies are required, our results suggest that femoroplasty can effectively reduce fracture risk for several osteolytic lesions in the femoral head/neck.

Finite Element Model-Computed Mechanical Behavior of Femurs with Metastatic Disease Varies Between Physiologic and Idealized Loading Simulations

Background and objectives:

Femurs affected by metastatic bone disease (MBD) frequently undergo surgery to prevent impending pathologic fractures due to clinician-perceived increases in fracture risk. Finite element (FE) models can provide more objective assessments of fracture risk. However, FE models of femurs with MBD have implemented strain- and strength-based estimates of fracture risk under a wide variety of loading configurations, and “physiologic” loading models typically simulate a single abductor force. Due to these variations, it is currently difficult to interpret mechanical fracture risk results across studies of femoral MBD. Our aims were to evaluate (1) differences in mechanical behavior between idealized loading configurations and those incorporating physiologic muscle forces, and (2) differences in the rankings of mechanical behavior between different loading configurations, in FE simulations to predict fracture risk in femurs with MBD.

Methods:

We evaluated 9 different patient-specific FE loading simulations for a cohort of 54 MBD femurs: strain outcome simulations—physiologic (normal walking [NW], stair ascent [SA], stumbling), and joint contact only (NW contact force, excluding muscle forces); strength outcome simulations—physiologic (NW, SA), joint contact only, offset torsion, and sideways fall. Tensile principal strain and femur strength were compared between simulations using statistical analyses.

Results:

Tensile principal strain was 26% higher ( R2 = 0.719, P < .001) and femur strength was 4% lower ( R2 = 0.984, P < .001) in simulations excluding physiologic muscle forces. Rankings of the mechanical predictions were correlated between the strain outcome simulations (ρ = 0.723 to 0.990, P < .001), and between strength outcome simulations (ρ = 0.524 to 0.984, P < .001).

Conclusions:

Overall, simulations incorporating physiologic muscle forces affected local strain outcomes more than global strength outcomes. Absolute values of strain and strength computed using idealized (no muscle forces) and physiologic loading configurations should be used within the appropriate context when interpreting fracture risk in femurs with MBD.

Finite element analysis potentially identifies nonessential prophylactic stabilization in femurs with metastatic disease

Metastatic bone disease (MBD) is often managed by non-specialized orthopedic surgeons who rely on Mirels’ criteria to predict pathologic fracture risk. However, low specificity of Mirels’ criteria implies many lesions are scored at high fracture risk when the actual mechanical fracture risk is minimal. Our goal was to retrospectively compare mechanical fracture risk in MBD patients to Mirels’ score and clinical treatment received. Using a CT-based finite element (FE) model of the proximal femur affected by MBD, femur strength and load-to-strength ratio (LSR) were determined for 52 femurs from 48 patients. Associations of femur strength with pain and Mirels’ scores (Pearson r/Spearman ρ correlations), and the decision to operate (percentile analysis), and associations of LSR with pain and Mirels’ scores (Spearman correlations) were determined. Nineteen of 52 femurs (37%) had a very low computed mechanical fracture risk (LSR < 0.4); 5 of those 19 underwent prophylactic stabilization, suggesting that clinical decision-making in MBD is substantially influenced by non-mechanical factors that likely overestimate pathologic fracture risk. Of the 30 femurs managed non-operatively, 24 had a low computed mechanical fracture risk (LSR ≤ 0.5), none of which (0%) experienced a fracture within 9 months. Patient-reported pain did not correlate with femur strength ( r = −0.05, p = 0.748) nor with LSR (ρ = 0.07, p = 0.632). Mirels’ score correlated weakly with femur strength (ρ = −0.32, p = 0.019) and with LSR (ρ = 0.29, p = 0.034). Computational mechanical tools like this FE model could be used as a clinical decision aid when considering non-surgical management in appropriate patients, potentially alleviating nonessential surgical treatment in some patients with femur MBD.

Two Cannulated Screws Provide Sufficient Biomechanical Strength for Prophylactic Fixation in Adult Patients With an Aggressive Benign Femoral Neck Lesion

Background: Two cannulated screws were proposed for prophylactic fixation in adult patients with an aggressive benign femoral neck lesion in recent literature. However, the biomechanical properties of this intervention have not yet been investigated.

Methods: After the evaluation of the heterogeneity of bone mineral density and geometry via quantitative computed tomography, 24 embalmed adult human cadaver femurs were randomized into the control, inferior half of the anterior cortical (25%) bone defect, entire anterior cortical (50%) bone defect, and the 50% bone defect and two cannulated screw group. Biomechanical analysis was conducted to compare the stiffness and failure load among the four groups when mimicking a one-legged stance. A CT-based finite element analysis (FEA) was performed to mimic the cortical and cancellous bone defect and the implantation of two cannulated screws of the four groups. Measurements of the maximal displacement and von Mises stress were conducted with the longitudinal load force and boundary conditions being established for a one-leg-standing status.

Results: We noted a significant improvement in the failure load after the insertion of two 6.5 mm cannulated screws in femurs with 50% bone defect (+95%, p = 0.048), and no significant difference was found between the screw group and the intact femur. Similar trends were also found in the measurements of stiffness (+23%, p &gt; 0.05) via biomechanical testing and the von Mises stresses (−71%, p = 0.043) by FEA when comparing the screw group and the 50% bone defect group.

Conclusion: Our findings suggest that two cannulated screws provided sufficient biomechanical strength for prophylactic fixation in adult patients with an aggressive benign femoral neck lesion even when the entire anterior cortical bone is involved.

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

Femoroplasty is a procedure where bone cement is injected percutaneously into a weakened proximal femur. Uncertainty exists whether femoroplasty provides sufficient mechanical strengthening to prevent fractures in patients with femoral bone metastases. Finite element models are promising tools to evaluate the mechanical effectiveness of femoroplasty, but a thorough validation is required. This study validated a voxel-based finite element model against experimental data from eight pairs of human cadaver femurs with artificial metastatic lesions. One femur from each pair was left untreated, while the contralateral femur was augmented with bone cement. Finite element models accurately predicted the femoral strength in the defect (R² = 0.96) and augmented (R² = 0.93) femurs. The modelled surface strain distributions showed a good qualitative match with results from digital image correlation; yet, quantitatively, only moderate correlation coefficients were found for the defect (mean R² = 0.78) and augmented (mean R² = 0.76) femurs. This was attributed to the presence of vessel holes in the femurs and the jagged surface representation of our voxel-based models. Despite some inaccuracies in the surface measurements, the FE models accurately predicted the global bone strength and qualitative deformation behavior, both before and after femoroplasty. Hence, they can offer a useful biomechanical tool to assist clinicians in assessing the need for prophylactic augmentation in patients with metastatic bone disease, as well as in identifying suitable patients for femoroplasty.

Fracture Risk Evaluation of Bone Metastases: A Burning Issue

Major progress has been achieved to treat cancer patients and survival has improved considerably, even for stage-IV bone metastatic patients. Locomotive health has become a crucial issue for patient autonomy and quality of life. The centerpiece of the reflection lies in the fracture risk evaluation of bone metastasis to guide physician decision regarding physical activity, antiresorptive agent prescription, and local intervention by radiotherapy, surgery, and interventional radiology. A key mandatory step, since bone metastases may be asymptomatic and disseminated throughout the skeleton, is to identify the bone metastasis location by cartography, especially within weight-bearing bones. For every location, the fracture risk evaluation relies on qualitative approaches using imagery and scores such as Mirels and spinal instability neoplastic score (SINS). This approach, however, has important limitations and there is a need to develop new tools for bone metastatic and myeloma fracture risk evaluation. Personalized numerical simulation qCT-based imaging constitutes one of these emerging tools to assess bone tumoral strength and estimate the femoral and vertebral fracture risk. The next generation of numerical simulation and artificial intelligence will take into account multiple loadings to integrate movement and obtain conditions even closer to real-life, in order to guide patient rehabilitation and activity within a personalized-medicine approach.

Which experimental procedures influence the apparent proximal femoral stiffness? A parametric study

Background

Experimental validation is the gold standard for the development of FE predictive models of bone. Employing multiple loading directions could improve this process. To capture the correct directional response of a sample, the effect of all influential parameters should be systematically considered. This study aims to determine the impact of common experimental parameters on the proximal femur’s apparent stiffness.

Methods

To that end, a parametric approach was taken to study the effects of: repetition, pre-loading, re-adjustment, re-fixation, storage, and μCT scanning as random sources of uncertainties, and loading direction as the controlled source of variation in both stand and side-fall configurations. Ten fresh-frozen proximal femoral specimens were prepared and tested with a novel setup in three consecutive sets of experiments. The neutral state and 15-degree abduction and adduction angles in both stance and fall configurations were tested for all samples and parameters. The apparent stiffness of the samples was measured using load-displacement data from the testing machine and validated against marker displacement data tracked by DIC cameras.

Results

Among the sources of uncertainties, only the storage cycle affected the proximal femoral apparent stiffness significantly. The random effects of setup manipulation and intermittent μCT scanning were negligible. The 15^∘ deviation in loading direction had a significant effect comparable in size to that of switching the loading configuration from neutral stance to neutral side-fall.

Conclusion

According to these results, comparisons between the stiffness of the samples under various loading scenarios can be made if there are no storage intervals between the different load cases on the same samples. These outcomes could be used as guidance in defining a highly repeatable and multi-directional experimental validation study protocol.

Fracture Risk of Long Bone Metastases: A Review of Current and New Decision-Making Tools for Prophylactic Surgery

Simple Summary

Long bone metastases are frequently a pivotal point in the oncological history of patients. Weakening of the bone results in pathologic fractures that not only compromise patient function but also their survival. Therefore, the main issue for tumor boards remains timely assessment of the risk of fracture, as this is a key consideration in providing preventive surgery while also avoiding overtreatment. As the Mirels scoring system takes into account both the radiological and the clinical criteria, it has been used worldwide since the 1990s. However, due to increasing concern regarding the lack of accuracy, new thresholds have been defined for the identification of impending fractures that require prophylactic surgery, on the basis of axial cortical involvement and biomechanical models involving quantitative computed tomography. The aim of this review is to establish a state-of-the-art of the risk assessment of long bone metastases fractures, from simple radiologic scores to more complex multidimensional bone models, in order to define new decision-making tools.

Abstract

Long bone pathological fractures very much reflect bone metastases morbidity in many types of cancer. Bearing in mind that they not only compromise patient function but also survival, identifying impending fractures before the actual event is one of the main concerns for tumor boards. Indeed, timely prophylactic surgery has been demonstrated to increase patient quality of life as well as survival. However, early surgery for long bone metastases remains controversial as the current fracture risk assessment tools lack accuracy. This review first focuses on the gold standard Mirels rating system. It then explores other unique imaging thresholds such as axial or circumferential cortical involvement and the merits of nuclear imaging tools. To overcome the lack of specificity, other fracture prediction strategies have focused on biomechanical models based on quantitative computed tomography (CT): computed tomography rigidity analysis (CT-RA) and finite element analysis (CT-FEA). Despite their higher specificities in impending fracture assessment, their limited availability, along with a need for standardization, have limited their use in everyday practice. Currently, the prediction of long bone pathologic fractures is a multifactorial process. In this regard, machine learning could potentially be of value by taking into account clinical survival prediction as well as clinical and improved CT-RA/FEA data.

Variabilities in µQCT-based FEA of a tumoral bone mice model

A finite element analysis based on Micro-Quantitative Computed Tomography (µQCT) is a method with high potential to improve fracture risk prediction. However, the segmentation process and model generation are generally not automatized in their entirety. Even with a rigorous protocol, the operator might add uncertainties during the creation of the model. The aim of this study was to evaluate a µQCT-based model of mice tumoral and sham tibias in terms of the variabilities induced by the operator and sensitivity to operator-dependent variables (such as model orientation or length). Two different operators generated finite element (FE) models from µCT images of 8 female Balb/c nude mice tibias aged 10 weeks old with bone tumors induced in the right tibia and with sham injection in the left. From these models, predicted failure load was determined for two different boundary conditions: fixed support and spherical joints. The difference between the predicted and experimental failure load of both operators was large (-122% to 93%). The difference in the predicted failure load between operators was less for the spherical joints boundary conditions (9.8%) than for the fixed support (58.3%), p < 0.001, whereas varying the orientation of bone tibia caused more variability for the fixed support boundary condition (44.7%) than for the spherical joints (9.1%), p < 0.002. Varying tibia length had no significant effect, regardless of boundary conditions (<4%). When using the same mesh and same orientation, the difference between operators is non-significant (<6%) for each model. This study showed that the operator influences the failure load assessed by a µQCT-based finite element model of the tumoral and sham mice tibias. The results suggest that automation is needed for better reproducibility.

Post-operative fracture risk assessment following tumor curettage in the distal femur: a hybrid in vitro and in silico biomechanical approach

The distal femur is the predominant site for benign bone tumours and a common site for fracture following tumour removal or cementation. However, the lack of conclusive assessment criterion for post-operative fracture risk and appropriate devices for cement augmentation are serious concerns. Hence, a validated biomechanical tool was developed to assess bone strength, depending on the size and location of artificially created tumorous defects in the distal femora. The mechanics of the bone–cement interface was investigated to determine the main causes of reconstruction failure. Based on quantitative-CT images, non-linear and heterogeneous finite element (FE) models of human cadaveric distal femora with simulated tumourous defects were created and validated using in vitro mechanical tests from 14 cadaveric samples. Statistical analyses demonstrated a strong linear relationship (R² = 0.95, slope = 1.12) with no significant difference between bone strengths predicted by in silico analyses and in vitro tests (P = 0.174). FE analyses showed little reduction in bone strength until the defect was 35% or more of epiphyseal volume, and reduction in bone strength was less pronounced for laterally located defects than medial side defects. Moreover, the proximal end of the cortical window and the most interior wall of the bone–cement interface were the most vulnerable sites for reconstruction failure.

Effect of CT imaging on the accuracy of the finite element modelling in bone

The finite element (FE) analysis is a highly promising tool to simulate the behaviour of bone. Skeletal FE models in clinical routine rely on the information about the geometry and bone mineral density distribution from quantitative computed tomography (CT) imaging systems. Several parameters in CT imaging have been reported to affect the accuracy of FE models. FE models of bone are exclusively developed in vitro under scanning conditions deviating from the clinical setting, resulting in variability of FE results (< 10%). Slice thickness and field of view had little effect on FE predicted bone behaviour (≤ 4%), while the reconstruction kernels showed to have a larger effect (≤ 20%). Due to large interscanner variations (≤ 20%), the translation from an experimental model into clinical reality is a critical step. Those variations are assumed to be mostly caused by different “black box” reconstruction kernels and the varying frequency of higher density voxels, representing cortical bone. Considering the low number of studies together with the significant effect of CT imaging on the finite element model outcome leading to high variability in the predicted behaviour, we propose further systematic research and validation studies, ideally preceding multicentre and longitudinal studies.

Fracture risk assessment and clinical decision making for patients with metastatic bone disease

Metastatic breast, prostate, lung, and other cancers often affect bone, causing pain, increasing fracture risk, and decreasing function. Management of metastatic bone disease (MBD) is clinically challenging when there is potential but uncertain risk of pathological fracture. Management of MBD has become a major focus within orthopedic oncology with respect to fracture and impending fracture care. If impending skeletal-related events (SREs), particularly pathologic fracture, could be predicted, increasing evidence suggests that prophylactic surgical treatment improves patient outcomes. However, current fracture risk assessment and radiographic metrics do not have high accuracy and have not been combined with relevant patient survival tools. This review first explores the prevalence, incidence, and morbidity of MBD and associated SREs for different cancer types. Strengths and limitations of current fracture risk scoring systems for spinal stability and long bone fracture are highlighted. More recent computed tomography (CT)-based structural rigidity analysis (CTRA) and finite element (FE) analysis methods offer advantages of increased specificity (true negative rate), but are limited in availability. Other fracture prediction approaches including parametric response mapping and positron emission tomography/computed tomography measures show early promise. Substantial new information to inform clinical decision-making includes measures of survival, clinical benefits, and economic analysis of prophylactic treatment compared to after-fracture stabilization. Areas of future research include use of big data and machine learning to predict SREs, greater access and refinement of CTRA/FE approaches, combination of clinical survival prediction tools with radiographically based fracture risk assessment, and net benefit analysis for fracture risk assessment and prophylactic treatment.

Finite element models for fracture prevention in patients with metastatic bone disease. A literature review

Patients with bone metastases have an increased risk to sustain a pathological fracture as lytic metastatic lesions damage and weaken the bone. In order to prevent fractures, prophylactic treatment is advised for patients with a high fracture risk. Mechanical stabilization of the femur can be provided through femoroplasty, a minimally invasive procedure where bone cement is injected into the lesion, or through internal fixation with intra- or extramedullary implants. Clinicians face the task of determining whether or not prophylactic treatment is required and which treatment would be the most optimal. Finite element (FE) models are promising tools that could support this decision process. The aim of this paper is to provide an overview of the state-of-the-art in FE modeling for the treatment decision of metastatic bone lesions in the femur. First, we will summarize the clinical and mechanical results of femoroplasty as a prophylactic treatment method. Secondly, current FE models for fracture risk assessment of metastatic femurs will be reviewed and the remaining challenges for clinical implementation will be discussed. Thirdly, we will elaborate on the simulation of femoroplasty in FE models and discuss future opportunities. Femoroplasty has already proven to effectively relieve pain and improve functionality, but there remains uncertainty whether it provides sufficient mechanical strengthening to prevent pathological fractures. FE models could help to select appropriate candidates for whom femoroplasty provides sufficient increase in strength and to further improve the mechanical benefit by optimizing the locations for cement augmentation.

Nonlinear voxel-based finite element model for strength assessment of healthy and metastatic proximal femurs

Nonlinear finite element (FE) models can accurately quantify bone strength in healthy and metastatic femurs. However, their use in clinical practice is limited since state-of-the-art implementations using tetrahedral meshes involve a lot of manual work for which specific modelling software and engineering knowledge are required. Voxel-based meshes could enable the transition since they are robust and can be highly automated. Therefore, the aim of this work was to bridge the modelling gap between the tetrahedral and voxel-based approach. Specifically, we validated a nonlinear voxel-based FE method relative to experimental data from 20 femurs with and without artificial metastases that had been mechanically loaded until failure. CT scans of the femurs were segmented and automatically converted into a voxel-based mesh with hexahedral elements. Nonlinear material properties were implemented in an open-source linear voxel-based FE solver by adding an additional loop to the routine such that the material properties could be adapted after each increment. Bone strength, quantified as the maximum force in the force-displacement curve, was evaluated. The results were compared to a previously established nonlinear tetrahedral FE approach as well as to the experimentally measured bone strength. The voxel-based FE model predicted the experimental bone strength very well both for healthy (R² = 0.90, RMSE = 0.88 kN) and metastatic femurs (R² = 0.93, RMSE = 0.64 kN). The model precision and accuracy were very similar to the ones obtained with the tetrahedral model (R² = 0.90/0.93, RMSE = 0.90/0.64 kN for intact/metastatic respectively). The more intuitive voxel-based meshes thus quantified macroscale femoral strength equally well as state-of-the-art tetrahedral models. The robustness, high level of automation and time-efficiency (< 30 min) of the implemented workflow offer great potential for developing FE models to improve fracture risk prediction in clinical practice.

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A nonlinear voxel-based FE model was evaluated to assess femoral bone strength

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Both healthy and metastatic femurs were evaluated

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The FE models predicted bone strength with high accuracy and precision

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Voxel-based and tetrahedral FE models showed similar accuracy and precision

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An iterative routine enabled material nonlinearity in a linear FE solver

Patient-specific finite element computer models improve fracture risk assessments in cancer patients with femoral bone metastases compared to clinical guidelines

PURPOSE

To determine whether patient-specific finite element (FE) computer models are better at assessing fracture risk for femoral bone metastases compared to clinical assessments based on axial cortical involvement on conventional radiographs, as described in current clinical guidelines.

METHODS

Forty-five patients with 50 femoral bone metastases, who were treated with palliative radiotherapy for pain, were included (64% single fraction (8Gy), 36% multiple fractions (5 or 6x4Gy)) and were followed for six months to determine whether they developed a pathological femoral fracture. All plain radiographs available within a two month period prior to radiotherapy were obtained. Patient-specific FE models were constructed based on the geometry and bone density obtained from the baseline quantitative CT scans used for radiotherapy planning. Femoral failure loads normalized for body weight (BW) were calculated. Patients with a failure load of 7.5 x BW or lower were identified as having high fracture risk, whereas patients with a failure load higher than 7.5 x BW were classified as low fracture risk. Experienced assessors measured axial cortical involvement on conventional radiographs. Following clinical guidelines, patients with lesions larger than 30mm were identified as having a high fracture risk. FE predictions were compared to clinical assessments by means of diagnostic accuracy values (sensitivity, specificity and positive (PPV) and negative predictive values (NPV)).

RESULTS

Seven femurs (14%) fractured during follow-up. Median time to fracture was 8 weeks. FE models were better at assessing fracture risk in comparison to axial cortical involvement (sensitivity 100% vs. 86%, specificity 74% vs. 42%, PPV 39% vs. 19%, and NPV 100% vs. 95%, for the FE computer model vs. axial cortical involvement, respectively).

CONCLUSIONS

Patient-specific FE computer models improve fracture risk assessments of femoral bone metastases in advanced cancer patients compared to clinical assessments based on axial cortical involvement, which is currently used in clinical guidelines.