Finite element analysis of the proximal femur and hip fracture risk in older men

DXA-based statistical models of shape and intensity outperform aBMD hip fracture prediction: A retrospective study

Areal bone mineral density (aBMD) currently represents the clinical gold standard for hip fracture risk assessment. Nevertheless, it is characterised by a limited prediction accuracy, as about half of the people experiencing a fracture are not classified as at being at risk by aBMD. In the context of a progressively ageing population, the identification of accurate predictive tools would be pivotal to implement preventive actions. In this study, DXA-based statistical models of the proximal femur shape, intensity (i.e., density) and their combination were developed and employed to predict hip fracture on a retrospective cohort of post-menopausal women. Proximal femur shape and pixel-by-pixel aBMD values were extracted from DXA images and partial least square (PLS) algorithm adopted to extract corresponding modes and components. Subsequently, logistic regression models were built employing the first three shape, intensity and shape-intensity PLS components, and their ability to predict hip fracture tested according to a 10-fold cross-validation procedure. The area under the ROC curves (AUC) for the shape, intensity, and shape-intensity-based predictive models were 0.59 (95%CI 0.47-0.69), 0.80 (95%CI 0.70-0.90) and 0.83 (95%CI 0.73-0.90), with the first being significantly lower than the latter two. aBMD yielded an AUC of 0.72 (95%CI 0.59-0.82), found to be significantly lower than the shape-intensity-based predictive model. In conclusion, a methodology to assess hip fracture risk uniquely based on the clinically available imaging technique, DXA, is proposed. Our study results show that hip fracture risk prediction could be enhanced by taking advantage of the full set of information DXA contains.

A new hip fracture risk index derived from FEA-computed proximal femur fracture loads and energies-to-failure

Hip fracture risk assessment is an important but challenging task. Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur. We developed a global FEA-computed fracture risk index to increase the prediction accuracy of hip fracture incidence.

PURPOSE

Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur to compute the force (fracture load) and energy necessary to break the proximal femur in a particular loading condition. The fracture loads and energies-to-failure are individually associated with incident hip fracture, and provide different structural information about the proximal femur.

METHODS

We used principal component analysis (PCA) to develop a global FEA-computed fracture risk index that incorporates the FEA-computed yield and ultimate failure loads and energies-to-failure in four loading conditions of 110 hip fracture subjects and 235 age- and sex-matched control subjects from the AGES-Reykjavik study. Using a logistic regression model, we compared the prediction performance for hip fracture based on the stratified resampling.

RESULTS

We referred the first principal component (PC1) of the FE parameters as the global FEA-computed fracture risk index, which was the significant predictor of hip fracture (p-value < 0.001). The area under the receiver operating characteristic curve (AUC) using PC1 (0.776) was higher than that using all FE parameters combined (0.737) in the males (p-value < 0.001).

CONCLUSIONS

The global FEA-computed fracture risk index increased hip fracture risk prediction accuracy in males.

3D-DXA Based Finite Element Modelling for Femur Strength Prediction: Evaluation Against QCT

Osteoporosis is characterised by the loss of bone density resulting in an increased risk of fragility fractures. The clinical gold standard for diagnosing osteoporosis is based on the areal bone mineral density (aBMD) used as a surrogate for bone strength, in combination with clinical risk factors. Finite element (FE) analyses based on quantitative computed tomography (QCT) have been shown to estimate bone strength better than aBMD. However, their application in the osteoporosis clinics is limited due to exposure of patients to increased X-rays radiation dose. Statistical modelling methods (3D-DXA) enabling the estimation of 3D femur shape and volumetric bone density from dual energy X-ray absorptiometry (DXA) scan have been shown to improve osteoporosis management. The current study used 3D-DXA based FE analyses to estimate femur strength from the routine clinical DXA scans and compared its results against 151 QCT based FE analyses, in a clinical cohort of 157 subjects. The linear regression between the femur strength predicted by QCT-FE and 3D-DXA-FE models correlated highly (coefficient of determination R2 = 0.86) with a root mean square error (RMSE) of 397 N. In conclusion, the current study presented a 3D-DXA-FE modelling tool providing accurate femur strength estimates noninvasively, compared to QCT-FE models.

Improved femoral micro-architecture in adult male individuals with overweight: fracture resistance due to regional specificities

BACKGROUND

It is still unclear whether femoral fracture risk is positively or negatively altered in individuals with overweight. Considering the lack of studies including men with overweight, this study aimed to analyze regional specificities in mechano-structural femoral properties (femoral neck and intertrochanteric region) in adult male cadavers with overweight compared to their normal-weight age-matched counterparts.

METHODS

Ex-vivo osteodensitometry, micro-computed tomography, and Vickers micro-indentation testing were performed on femoral samples taken from 30 adult male cadavers, divided into the group with overweight (BMI between 25 and 30 kg/m2; n = 14; age:55 ± 16 years) and control group (BMI between 18.5 and 25 kg/m2; n = 16; age:51 ± 18 years).

RESULTS

Better quality of trabecular and cortical microstructure in the inferomedial (higher trabecular bone volume fraction, trabecular thickness, and cortical thickness, coupled with reduced cortical pore diameter, p < 0.05) and superolateral femoral neck (higher trabecular number and tendency to lower cortical porosity, p = 0.043, p = 0.053, respectively) was noted in men with overweight compared to controls. Additionally, the intertrochanteric region of men with overweight had more numerous and denser trabeculae, coupled with a thicker and less porous cortex (p < 0.05). Still, substantial overweight-induced change in femoral osteodensitometry parameters and Vickers micro-hardness was not demonstrated in assessed femoral subregions (p > 0.05).

CONCLUSIONS

Despite the absence of significant changes in femoral osteodensitometry, individuals with overweight had better trabecular and cortical femoral micro-architecture implying higher femoral fracture resistance. However, the microhardness was not significantly favorable in the individuals who were overweight, indicating the necessity for further research.

QCT-based 3D finite element modeling to assess patient-specific hip fracture risk and risk factors

Early assessment of hip fracture risk may play a critical role in designing preventive mechanisms to reduce the occurrence of hip fracture in geriatric people. The loading direction, clinical, and morphological variables play a vital role in hip fracture. Analyzing the effects of these variables helps predict fractures risk more accurately; thereby suggesting the critical variable that needs to be considered. Hence, this work considered the fall postures by varying the loading direction on the coronal plane (α) and on the transverse plane (β) along with the clinical variables-age, sex, weight, and bone mineral density, and morphological variables-femoral neck axis length, femoral neck width, femoral neck angle, and true moment arm. The strain distribution obtained via finite element analysis (FEA) shows that the angle of adduction (α) during a fall increases the risk of fracture at the greater trochanter and femoral neck, whereas with an increased angle of rotation (β) during the fall, the FRI increases by ∼1.35 folds. The statistical analysis of clinical, morphological, and loading variables (αandβ) delineates that the consideration of only one variable is not enough to realistically predict the possibility of fracture as the correlation between individual variables and FRI is less than 0.1, even though they are shown to be significant (p<0.01). On the contrary, the correlation (R2=0.48) increases as all variables are considered, suggesting the need for considering different variables fork predicting FRI. However, the effect of each variable is different. While loading, clinical, and morphological variables are considered together, the loading direction on transverse plane (β) has high significance, and the anatomical variabilities have no significance.

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.

An analytical model of lateral condylar plate working length

BACKGROUND

The locking plate is a common device to treat distal femur fractures. Healing is affected by construct stiffness, thus many surgeon-controlled variables such as working length have been examined for their effects on strain at the fracture. No convenient analytical model which aids surgeons in determining working length has yet been described. We propose an analytical model and compare it to finite element analysis and cadaveric biomechanical testing.

METHODS

First, an analytical model based on a cantilever beam equation was derived. Next, a finite element model was developed based on a CT scan of a "fresh-frozen" cadaveric femur. Third, biomechanical testing in single-leg stance loading was performed on the cadaver. In all methods, strain at the fracture was recorded. An ANCOVA test was conducted to compare the strains.

FINDINGS

In all models, as the working length increased so did strain. For strain at the fracture, the shortest working length (35 mm) had a strain of 8% in the analytical model, 9% in the finite element model, and 7% for the cadaver. The longest working length (140 mm) demonstrated strain of 15% in the analytical model, and the finite element and biomechanical tests both demonstrated strain of 14%.

INTERPRETATION

The strain predicted by the analytical model was consistent with the strain observed in both the finite element and biomechanical models. As demonstrated in existing literature, increasing the working length increases strain at the fracture site. Additional work is required to refine and establish validity and reliability of the analytical model.

Validation of Biomechanical Computed Tomography for Fracture Risk Classification in Metastatic Hormone-sensitive Prostate Cancer

BACKGROUND

Guidelines recommend dual-energy x-ray absorptiometry (DXA) screening to assess fracture risk and benefit from antiresorptive therapy in men with metastatic hormone-sensitive prostate cancer (mHSPC) on androgen deprivation therapy (ADT). However, <30% of eligible patients undergo DXA screening. Biomechanical computed tomography (BCT) is a radiomic technique that measures bone mineral density (BMD) and bone strength from computed tomography (CT) scans.

OBJECTIVE

To evaluate the (1) correlations between BCT- and DXA-assessed BMD, and (2) associations between BCT-assessed metrics and subsequent fracture.

DESIGN, SETTING, AND PARTICIPANTS

A multicenter retrospective cohort study was conducted among patients with mHSPC between 2013 and 2020 who received CT abdomen/pelvis or positron emission tomography/CT within 48 wk before ADT initiation and during follow-up (48-96 wk after ADT initiation).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

We used univariate logistic regression to assess the associations between BCT measurements and the primary outcomes of subsequent pathologic and nonpathologic fractures.

RESULTS AND LIMITATIONS

Among 91 eligible patients, the median ([interquartile range) age was 67 yr (62-75), 44 (48.4%) were White, and 41 (45.1%) were Black. During the median follow-up of 82 wk, 17 men (18.6%) developed a pathologic and 15 (16.5%) a nonpathologic fracture. BCT- and DXA-assessed femoral-neck BMD T scores were strongly correlated (R2 = 0.93). On baseline CT, lower BCT-assessed BMD (odds ratio [OR] 1.80, 95% confidence interval or CI [1.10, 3.25], p = 0.03) was associated with an increased risk of a pathologic fracture. Lower femoral strength (OR 1.63, 95% CI [0.99, 2.71], p = 0.06) was marginally associated with an increased risk of a pathologic fracture. Neither BMD (OR 1.52, 95% CI [0.95, 2.63], p = 0.11) nor strength (OR 1.14, 95% CI [0.75, 1.80], p = 0.57) was associated with a nonpathologic fracture. BCT identified nine (9.9%) men eligible for antiresorptive therapy, of whom four (44%) were not treated. Limitations include low fracture numbers resulting in lower power to detect fracture associations.

CONCLUSIONS

Among men diagnosed with mHSPC, BCT assessments were strongly correlated with DXA, predicted subsequent pathologic fracture, and identified additional men indicated for antiresorptive therapy.

PATIENT SUMMARY

We assess whether biomechanical computer tomography (BCT) from routine computer tomography (CT) scans can identify fracture risk among patients recently diagnosed with metastatic prostate cancer. We find that BCT and dual-energy x-ray absorptiometry-derived bone mineral density are strongly correlated and that BCT accurately identifies the risk for future fracture. BCT may enable broader fracture risk assessment and facilitate timely interventions to reduce fracture risk in metastatic prostate cancer patients.

3D Finite Element Models Reconstructed From 2D Dual-Energy X-Ray Absorptiometry (DXA) Images Improve Hip Fracture Prediction Compared to Areal BMD in Osteoporotic Fractures in Men (MrOS) Sweden Cohort

Bone strength is an important contributor to fracture risk. Areal bone mineral density (aBMD) derived from dual-energy X-ray absorptiometry (DXA) is used as a surrogate for bone strength in fracture risk prediction tools. 3D finite element (FE) models predict bone strength better than aBMD, but their clinical use is limited by the need for 3D computed tomography and lack of automation. We have earlier developed a method to reconstruct the 3D hip anatomy from a 2D DXA image, followed by subject-specific FE-based prediction of proximal femoral strength. In the current study, we aim to evaluate the method's ability to predict incident hip fractures in a population-based cohort (Osteoporotic Fractures in Men [MrOS] Sweden). We defined two subcohorts: (i) hip fracture cases and controls cohort: 120 men with a hip fracture (<10 years from baseline) and two controls to each hip fracture case, matched by age, height, and body mass index; and (ii) fallers cohort: 86 men who had fallen the year before their hip DXA scan was acquired, 15 of which sustained a hip fracture during the following 10 years. For each participant, we reconstructed the 3D hip anatomy and predicted proximal femoral strength in 10 sideways fall configurations using FE analysis. The FE-predicted proximal femoral strength was a better predictor of incident hip fractures than aBMD for both hip fracture cases and controls (difference in area under the receiver operating characteristics curve, ΔAUROC = 0.06) and fallers (ΔAUROC = 0.22) cohorts. This is the first time that FE models outperformed aBMD in predicting incident hip fractures in a population-based prospectively followed cohort based on 3D FE models obtained from a 2D DXA scan. Our approach has potential to notably improve the accuracy of fracture risk predictions in a clinically feasible manner (only one single DXA image is needed) and without additional costs compared to the current clinical approach. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Assessing the risk of re-fracture related to the percentage of partial union in scaphoid waist fractures

Aims

There is ambiguity surrounding the degree of scaphoid union required to safely allow mobilization following scaphoid waist fracture. Premature mobilization could lead to refracture, but late mobilization may cause stiffness and delay return to normal function. This study aims to explore the risk of refracture at different stages of scaphoid waist fracture union in three common fracture patterns, using a novel finite element method.

Methods

The most common anatomical variant of the scaphoid was modelled from a CT scan of a healthy hand and wrist using 3D Slicer freeware. This model was uploaded into COMSOL Multiphysics software to enable the application of physiological enhancements. Three common waist fracture patterns were produced following the Russe classification. Each fracture had differing stages of healing, ranging from 10% to 90% partial union, with increments of 10% union assessed. A physiological force of 100 N acting on the distal pole was applied, with the risk of refracture assessed using the Von Mises stress.

Results

Overall, 90% to 30% fracture unions demonstrated a small, gradual increase in the Von Mises stress of all fracture patterns (16.0 MPa to 240.5 MPa). All fracture patterns showed a greater increase in Von Mises stress from 30% to 10% partial union (680.8 MPa to 6,288.6 MPa).

Conclusion

Previous studies have suggested 25%, 50%, and 75% partial union as sufficient for resuming hand and wrist mobilization. This study shows that 30% union is sufficient to return to normal hand and wrist function in all three fracture patterns. Both 50% and 75% union are unnecessary and increase the risk of post-fracture stiffness. This study has also demonstrated the feasibility of finite element analysis (FEA) in scaphoid waist fracture research. FEA is a sustainable method which does not require the use of finite scaphoid cadavers, hence increasing accessibility into future scaphoid waist fracture-related research.

Examining agreement between finite element modelling methodologies in predicting pathological fracture risk in proximal femurs with bone metastases

BACKGROUND

Finite element modelling methodologies available for assessing femurs with metastases accurately predict strength and pathological fracture risk which has led them to being considered for implementation into the clinic. However, the models available use varying material models, loading conditions, and critical thresholds. The aim of this study was to determine the agreement between finite element modelling methodologies in assessing fracture risk in proximal femurs with metastases.

METHODS

CT images of the proximal femur were obtained of 7 patients who presented with a pathologic femoral fracture (fracture group) and the contralateral femur of 11 patients scheduled for prophylactic surgery (non-fracture group). Fracture risk was predicted for each patient following three established finite modelling methodologies which have previously shown to accurately predict strength and determine fracture risk: non-linear isotropic -based model, strain fold ratio -based model, Hoffman failure criteria -based model.

FINDINGS

The methodologies demonstrated good diagnostic accuracy in assessing fracture risk (AUC = 0.77, 0.73, and 0.67). There was a stronger monotonic association between the non-linear isotropic and Hoffman -based models (τ = 0.74) than with the strain fold ratio model (τ = -0.24 and - 0.37). There was moderate or low agreement between methodologies in discriminating between individuals at high or low risk of fracture (κ = 0.20, 0.39, and 0.62).

INTERPRETATION

The present results suggest there may be a lack of consistency in the management of pathological fractures in the proximal femur based on the finite element modelling methodologies.

Prevalence of osteoporosis in older male veterans receiving hip-containing computed tomography scans: opportunistic use of biomechanical computed tomography analysis (BCT)

Osteoporosis care in men is suboptimal due to low rates of testing and treatment. Applying biomechanical computed tomography (BCT) analysis to existing CT scans, we found a high proportion of men with osteoporosis have never been diagnosed or treated. BCT may improve identification of patients at high risk of fracture.

PURPOSE

Osteoporosis care in men is suboptimal due to low rates of DXA testing and treatment. Biomechanical computed tomography analysis (BCT) can be applied "opportunistically" to prior hip-containing CT scans to measure femoral bone strength and hip BMD.

METHODS

In this retrospective, cross-sectional study, we used BCT in male veterans with existing CT scans to investigate the prevalence of osteoporosis, defined by hip BMD (T-score ≤  - 2.5) or fragile bone strength (≤ 3500 N). 577 men, age ≥ 65 with abdominal/pelvic CTs performed in 2017-2019, were randomly selected for BCT analysis. Clinical data were collected via electronic health records and used with the femoral neck BMD T-score from BCT to estimate 10-year hip fracture risks by FRAX.

RESULTS

Prevalence of osteoporosis by BCT increased with age (13.5% age 65-74; 18.2% age 75-84; 34.3% age ≥ 85), with an estimated overall prevalence of 18.3% for men age ≥ 65. In those with osteoporosis (n = 108/577), only 38.0% (41/108) had a prior DXA and 18.6% (7/108) had received osteoporosis pharmacotherapy. Elevated hip fracture risk by FRAX (≥ 3%) did not fully capture those with fragile bone strength. In a multivariate logistic regression model adjusted for age, BMI, race, and CT location, end stage renal disease (odds ratio 7.4; 95% confidence interval 2.3-23.9), COPD (2.2; 1.2-4.0), and high-dose inhaled corticosteroid use (3.7; 1.2-11.8) were associated with increased odds of having osteoporosis by BCT.

CONCLUSION

Opportunistic BCT in male veterans provides an additional avenue to identify patients who are at high risk of fractures.

Prediction of femoral strength of elderly men based on quantitative computed tomography images using machine learning

Hip fracture is the most common complication of osteoporosis, and its major contributor is compromised femoral strength. This study aimed to develop practical machine learning models based on clinical quantitative computed tomography (QCT) images for predicting proximal femoral strength. Eighty subjects with entire QCT data of the right hip region were randomly selected from the full MrOS cohorts, and their proximal femoral strengths were calculated by QCT-based finite element analysis (QCT/FEA). A total of 50 parameters of each femur were extracted from QCT images as the candidate predictors of femoral strength, including grayscale distribution, regional cortical bone mapping (CBM) measurements, and geometric parameters. These parameters were simplified by using feature selection and dimensionality reduction. Support vector regression (SVR) was used as the machine learning algorithm to develop the prediction models, and the performance of each SVR model was quantified by the mean squared error (MSE), the coefficient of determination (R² ), the mean bias, and the SD of bias. For feature selection, the best prediction performance of SVR models was achieved by integrating the grayscale value of 30% percentile and specific regional CBM measurements (MSE ≤ 0.016, R² ≥ 0.93); and for dimensionality reduction, the best prediction performance of SVR models was achieved by extracting principal components with eigenvalues greater than 1.0 (MSE ≤ 0.014, R² ≥ 0.93). The femoral strengths predicted from the well-trained SVR models were in good agreement with those derived from QCT/FEA. This study provided effective machine learning models for femoral strength prediction, and they may have great potential in clinical bone health assessments.

2D-3D reconstruction of the proximal femur from DXA scans: Evaluation of the 3D-Shaper software

Introduction: Osteoporosis is currently diagnosed based on areal bone mineral density (aBMD) computed from 2D DXA scans. However, aBMD is a limited surrogate for femoral strength since it does not account for 3D bone geometry and density distribution. QCT scans combined with finite element (FE) analysis can deliver improved femoral strength predictions. However, non-negligible radiation dose and high costs prevent a systematic usage of this technique for screening purposes. As an alternative, the 3D-Shaper software (3D-Shaper Medical, Spain) reconstructs the 3D shape and density distribution of the femur from 2D DXA scans. This approach could deliver a more accurate estimation of femoral strength than aBMD by using FE analysis on the reconstructed 3D DXA. Methods: Here we present the first independent evaluation of the software, using a dataset of 77 ex vivo femora. We extend a prior evaluation by including the density distribution differences, the spatial correlation of density values and an FE analysis. Yet, cortical thickness is left out of this evaluation, since the cortex is not resolved in our FE models. Results: We found an average surface distance of 1.16 mm between 3D DXA and QCT images, which shows a good reconstruction of the bone geometry. Although BMD values obtained from 3D DXA and QCT correlated well (r ² = 0.92), the 3D DXA BMD were systematically lower. The average BMD difference amounted to 64 mg/cm³, more than one-third of the 3D DXA BMD. Furthermore, the low correlation (r ² = 0.48) between density values of both images indicates a limited reconstruction of the 3D density distribution. FE results were in good agreement between QCT and 3D DXA images, with a high coefficient of determination (r ² = 0.88). However, this correlation was not statistically different from a direct prediction by aBMD. Moreover, we found differences in the fracture patterns between the two image types. QCT-based FE analysis resulted mostly in femoral neck fractures and 3D DXA-based FE in subcapital or pertrochanteric fractures. Discussion: In conclusion, 3D-Shaper generates an altered BMD distribution compared to QCT but, after careful density calibration, shows an interesting potential for deriving a standardized femoral strength from a DXA scan.

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.

Clinical Assessments of Fracture Healing and Basic Science Correlates: Is There Room for Convergence?

PURPOSE OF REVIEW

The purpose of this review is to summarize the clinical and basic science methods used to assess fracture healing and propose a framework to improve the translational possibilities.

RECENT FINDINGS

Mainstays of fracture healing assessment include clinical examination, various imaging modalities, and assessment of function. Pre-clinical studies have yielded insight into biomechanical progression as well as the genetic, molecular, and cellular processes of fracture healing. Efforts are emerging to identify early markers to predict impaired healing and possibly early intervention to alter these processes. Despite of the differences in clinical and preclinical research, opportunities exist to unify and improve the translational efforts between these arenas to develop and optimize our ability to assess and predict fracture healing, thereby improving the clinical care of these patients.

Personalizing Revision Tibial Baseplate Position and Stem Trajectory With Custom Implants Using 3D Modeling to Optimize Press-fit Stem Placement

Background

A common tibial construct for revision total knee arthroplasty includes a long diaphyseal engaging press-fit stem. Due to tibial canal bowing, compromises are often necessary to match patient anatomy when choosing stemmed implants. The objective of this study is to determine through 3-D modeling whether current implant press-fit options appropriately fit patient anatomy, or whether an alternative angle between the stem and baseplate could increase the cortical engagement of long press-fit tibial stems.

Methods

Preoperative computerized tomography scans from 100 patients undergoing TKA were imported into an image-processing software program. Three-dimensional models were created with tibial stems placed at a fixed perpendicular angle and a custom angle to the revision tibial baseplate. Stem diameter, depth, offset, and contact surface area were measured and analyzed between the 2 groups.

Results

Significantly more cortical contact, larger stem diameter, and smaller offset of the custom keel from the center of the baseplate were associated with free custom tibial stem placement vs a fixed perpendicular baseplate-stem interface (P < .001). Statistically significant differences were also found between different patient demographics.

Conclusions

Custom free-angle stem placement allows for increased stem diameter and cortical contact of press-fit tibial stems compared to existing constructs that must interface with the baseplate at a 90-degree angle. Current revision tibia implants limit fixation of tibial press-fit stems and often mismatch with patient anatomy. Alternative ways to fit patient anatomy may be beneficial for patients with extreme mismatch. In the future, custom keel angles may help to resolve this problem.

An open-access plug-in program for 3D modelling distinct material properties of cortical and trabecular bone

BACKGROUND

Finite element modelling the material behavior of bone in-silico is a powerful tool to predict the best suited surgical treatment for individual patients.

RESULTS

We demonstrate the development and use of a pre-processing plug-in program with a 3D modelling image processing software suite (Synopsys Simpleware, ScanIP) to assist with identifying, isolating, and defining cortical and trabecular bone material properties from patient specific computed tomography scans. The workflow starts by calibrating grayscale values of each constituent element with a phantom - a standardized object with defined densities. Using an established power law equation, we convert the apparent density value per voxel to a Young's Modulus. The resulting "calibrated" scan can be used for modeling and in-silico experimentation with Finite Element Analysis.

CONCLUSIONS

This process allows for the creation of realistic and personalized simulations to inform a surgeon's decision-making. We have made this plug-in program open and accessible as a supplemental file.

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.

Biomechanical Assessment of Cannulated Nails for the Treatment of Proximal Femur Fractures

This article focuses on a type of surgical implant used in orthopaedics and traumatology—cannulated femoral nails. Femoral nails are used in medical treatment for purposes of osteosynthesis, i.e., when treating various types of complicated fractures, in this case fractures of the femur. The article investigates cases in which a nail has been implanted in the proximal part of the femur for a short time (with the fracture still not healed), compared with cases in which the bone has already healed. According to AO classification, examined fractures are described as AO 31B3 AO 32A3. The main focus is on strength-deformation analysis using the finite element method (FEM), which makes it possible to determine the behaviour of the femur-implant system. FEM analysis was used to compare 1.4441 steel nails made by two manufacturers, Medin (Czech Republic) and Tantum (Germany). Boundary conditions including external loading, prescribed supports and elastic foundation are defined. There were solved FEM analyses for five cases of healed femur and five cases of broken femur both including implants with prescribed collo-diaphyseal angles. The results of the analysis were used to assess stress-deformation states from the perspective of appropriateness for clinical treatment, biomechanical reliability and safety. All examined femoral nails are compared, safe and suitable for patient treatment.

Reproducibility of Densitometric and Biomechanical Assessment of the Mouse Tibia From In Vivo Micro-CT Images

Interventions for bone diseases (e.g. osteoporosis) require testing in animal models before clinical translation and the mouse tibia is among the most common tested anatomical sites. In vivo micro-Computed Tomography (microCT) based measurements of the geometrical and densitometric properties are non-invasive and therefore constitute an important tool in preclinical studies. Moreover, validated micro-Finite Element (microFE) models can be used for predicting the bone mechanical properties non-invasively. However, considering that the image processing pipeline requires operator-dependant steps, the reproducibility of these measurements has to be assessed. The aim of this study was to evaluate the intra- and inter-operator reproducibility of several bone parameters measured from microCT images. Ten in vivo microCT images of the right tibia of five mice (at 18 and 22 weeks of age) were processed. One experienced operator (intra-operator analysis) and three different operators (inter-operator) aligned each image to a reference through a rigid registration and selected a volume of interest below the growth plate. From each image the following parameters were measured: total bone mineral content (BMC) and density (BMD), BMC in 40 subregions (ten longitudinal sections, four quadrants), microFE-based stiffness and failure load. Intra-operator reproducibility was acceptable for all parameters (precision error, PE < 3.71%), with lowest reproducibility for stiffness (3.06% at week 18, 3.71% at week 22). The inter-operator reproducibility was slightly lower (PE < 4.25%), although still acceptable for assessing the properties of most interventions. The lowest reproducibility was found for BMC in the lateral sector at the midshaft (PE = 4.25%). Densitometric parameters were more reproducible than most standard morphometric parameters calculated in the proximal trabecular bone. In conclusion, microCT and microFE models provide reproducible measurements for non-invasive assessment of the mouse tibia properties.

Finite element derived femoral strength is a better predictor of hip fracture risk than aBMD in the AGES Reykjavik study cohort

Hip fractures associated with a high economic burden, loss of independence, and a high rate of post-fracture mortality, are a major health concern for modern societies. Areal bone mineral density is the current clinical metric of choice when assessing an individual's future risk of fracture. However, this metric has been shown to lack sensitivity and specificity in the targeted selection of individuals for preventive interventions. Although femoral strength derived from computed tomography based finite element models has been proposed as an alternative based on its superior femoral strength prediction ex vivo, such predictions have only shown marginal or no improvement for assessing hip fracture risk. This study compares finite element derived femoral strength to aBMD as a metric for hip fracture risk assessment in subjects (N = 601) from the AGES Reykjavik Study cohort and analyses the dependence of femoral strength predictions and classification accuracy on the material model and femoral loading alignment. We found hip fracture classification based on finite element derived femoral strength to be significantly improved compared to aBMD. Finite element models with non-linear material models performed better at classifying hip fractures compared to finite element models with linear material models and loading alignments with low internal rotation and adduction, which do not correspond to weak femur alignments, were found to be most suitable for hip fracture classification.

Finite Element Assessment of Bone Fragility from Clinical Images

PURPOSE OF REVIEW

We re-evaluated clinical applications of image-to-FE models to understand if clinical advantages are already evident, which proposals are promising, and which questions are still open.

RECENT FINDINGS

CT-to-FE is useful in longitudinal treatment evaluation and groups discrimination. In metastatic lesions, CT-to-FE strength alone accurately predicts impending femoral fractures. In osteoporosis, strength from CT-to-FE or DXA-to-FE predicts incident fractures similarly to DXA-aBMD. Coupling loads and strength (possibly in dynamic models) may improve prediction. One promising MRI-to-FE workflow may now be tested on clinical data. Evidence of artificial intelligence usefulness is appearing. CT-to-FE is already clinical in opportunistic CT screening for osteoporosis, and risk of metastasis-related impending fractures. Short-term keys to improve image-to-FE in osteoporosis may be coupling FE with fall risk estimates, pool FE results with other parameters through robust artificial intelligence approaches, and increase reproducibility and cross-validation of models. Modeling bone modifications over time and bone fracture mechanics are still open issues.

Bone mineral density modeling via random field: Normality, stationarity, sex and age dependence

BACKGROUND AND OBJECTIVE

Capturing the population variability of bone properties is of paramount importance to biomedical engineering. The aim of the present paper is to describe variability and correlations in bone mineral density with a spatial random field inferred from routine computed tomography data.

METHODS

Random fields were simulated by transforming pairwise uncorrelated Gaussian random variables into correlated variables through the spectral decomposition of an age-detrended correlation matrix. The validity of the random field model was demonstrated in the spatiotemporal analysis of bone mineral density. The similarity between the computed tomography samples and those generated via random fields was analyzed with the energy distance metric.

RESULTS

The random field of bone mineral density was found to be approximately Gaussian/slightly left-skewed/strongly right-skewed at various locations. However, average bone density could be simulated well with the proposed Gaussian random field for which the energy distance, i.e., a measure that quantifies discrepancies between two distribution functions, is convergent with respect to the number of correlation eigenpairs.

CONCLUSIONS

The proposed random field model allows the enhancement of computational biomechanical models with variability in bone mineral density, which could increase the usability of the model and provides a step forward in in-silico medicine.

Bringing Mechanical Context to Image-Based Measurements of Bone Integrity

PURPOSE OF REVIEW

Image-based measurements of bone integrity are used to estimate failure properties and clinical fracture risk. This paper (1) reviews recent imaging studies that have enhanced our understanding of the mechanical pathways to bone fracture and (2) discusses the influence that inter-individual differences in image-based measurements may have on the clinical assessment of fracture risk RECENT FINDINGS: Increased tissue mineralization is associated with improved bone strength but reduced fracture toughness. Trabecular architecture that is important for fatigue resistance is less important for bone strength. The influence of porosity on bone failure properties is heavily dependent on pore location and size. The interaction of various characteristics, such as bone area and mineral content, can further complicate their influence on bone failure properties. What is beneficial for bone strength is not always beneficial for bone toughness or fatigue resistance. Additionally, given the large amount of imaging data that is clinically available, there is a need to develop effective translational strategies to better interpret non-invasive measurements of bone integrity.

Assessing hip fracture risk in type-2 diabetic patients using CT-based autonomous finite element methods : a feasibility study

AIMS

Type 2 diabetes mellitus (T2DM) impairs bone strength and is a significant risk factor for hip fracture, yet currently there is no reliable tool to assess this risk. Most risk stratification methods rely on bone mineral density, which is not impaired by diabetes, rendering current tests ineffective. CT-based finite element analysis (CTFEA) calculates the mechanical response of bone to load and uses the yield strain, which is reduced in T2DM patients, to measure bone strength. The purpose of this feasibility study was to examine whether CTFEA could be used to assess the hip fracture risk for T2DM patients.

METHODS

A retrospective cohort study was undertaken using autonomous CTFEA performed on existing abdominal or pelvic CT data comparing two groups of T2DM patients: a study group of 27 patients who had sustained a hip fracture within the year following the CT scan and a control group of 24 patients who did not have a hip fracture within one year. The main outcome of the CTFEA is a novel measure of hip bone strength termed the Hip Strength Score (HSS).

RESULTS

The HSS was significantly lower in the study group (1.76 (SD 0.46)) than in the control group (2.31 (SD 0.74); p = 0.002). A multivariate model showed the odds of having a hip fracture were 17 times greater in patients who had an HSS ≤ 2.2. The CTFEA has a sensitivity of 89%, a specificity of 76%, and an area under the curve of 0.90.

CONCLUSION

This preliminary study demonstrates the feasibility of using a CTFEA-based bone strength parameter to assess hip fracture risk in a population of T2DM patients. Cite this article: Bone Joint J 2021;103-B(9):1497-1504.

On challenges in clinical assessment of hip fracture risk using image-based biomechanical modelling: a critical review

INTRODUCTION

Hip fracture is a common health risk among elderly people, due to the prevalence of osteoporosis and accidental fall in the population. Accurate assessment of fracture risk is a crucial step for clinicians to consider patient-by-patient optimal treatments for effective prevention of fractures. Image-based biomechanical modeling has shown promising progress in assessment of fracture risk, and there is still a great possibility for improvement. The purpose of this paper is to identify key issues that need be addressed to improve image-based biomechanical modeling.

MATERIALS AND METHODS

We critically examined issues in consideration and determination of the four biomechanical variables, i.e., risk of fall, fall-induced impact force, bone geometry and bone material quality, which are essential for prediction of hip fracture risk. We closely inspected: limitations introduced by assumptions that are adopted in existing models; deficiencies in methods for construction of biomechanical models, especially for determination of bone material properties from bone images; problems caused by separate use of the variables in clinical study of hip fracture risk; availability of clinical information that are required for validation of biomechanical models.

RESULTS AND CONCLUSIONS

A number of critical issues and gaps were identified. Strategies for effectively addressing the issues were discussed.

Femoral strength and strains in sideways fall: Validation of finite element models against bilateral strain measurements

Low impact falls to the side are the main cause of hip fractures in elderly. Finite element (FE) models of the proximal femur may help in the assessment of patients at high risk for a hip fracture. However, extensive validation is essential before these models can be used in a clinical setting. This study aims to use strain measurements from bilateral digital image correlation to validate an FE model against ex vivo experimental data of proximal femora under a sideways fall loading condition. For twelve subjects, full-field strain measurements were available on the medial and lateral side of the femoral neck. In this study, subject-specific FE models were generated based on a consolidated procedure previously validated for stance loading. The material description included strain rate dependency and separate yield and fracture strain limits in tension and compression. FE predicted fracture force and experimentally measured peak forces showed a strong correlation (R² = 0.92). The FE simulations predicted the fracture initiation within 3 mm distance of the experimental fracture line for 8/12 subjects. The predicted and measured strains correlated well on both the medial side (R² = 0.87) and the lateral side (R² = 0.74). The lower correlation on the lateral side is attributed to the irregularity of the cortex and presence of vessel holes in this region. The combined validation against bilateral full-field strain measurements and peak forces has opened the door for a more elaborate qualitative and quantitative validation of FE models of femora under sideways fall loading.

Testosterone therapy and bone quality in men with diabetes and hypogonadism: Study design and protocol

Context

Type 2 diabetes mellitus (T2D) is often accompanied by male hypogonadism and both conditions are associated with increased risk for fractures. Testosterone (T) has been shown to improve the bone health of hypogonadal men but has not been tested in patients who also have T2D in addition to low T. To date, there is no treatment that is specifically recommended for bone disease among patients with T2D. This study will evaluate the effect of T therapy on the bone health of male veterans with low T who also have T2D.

Methods

This is a randomized double-blind placebo-controlled trial of 166 male veterans 35–65 years old, with T2D and hypogonadism, randomized to either T gel 1.62% or placebo for 12 months. We will evaluate the effect of T therapy on the following primary outcomes:1) changes in bone strength as measured by microfinite elements analysis (μFEA) using high-resolution peripheral quantitative computer tomography, 2) changes in bone turnover markers, and 3) changes in circulating osteoblast progenitors (COP) and osteoclast precursors cells.

Discussion

We anticipate that T therapy will result in improvement in bone strength owing to improvement in bone remodeling through an increase in osteoblastic differentiation and proliferation in patients with hypogonadism and T2D.

Triple-Phase Computed Tomography May Replace Dual-Energy X-ray Absorptiometry Scan for Evaluation of Osteoporosis in Liver Transplant Candidates

Assessment of bone density is an important part of liver transplantation (LT) evaluation for early identification and treatment of osteoporosis. Dual-energy X-ray absorptiometry (DXA) is currently the standard clinical test for osteoporosis; however, it may contribute to the appointment burden on LT candidates during the cumbersome evaluation process, and there are limitations affecting its accuracy. In this study, we evaluate the utility of biomechanical analysis of vertebral images obtained during dual-energy abdominal triple-phase computed tomography (TPCT) in diagnosing osteoporosis among LT candidates. We retrospectively reviewed cases evaluated for LT between January 2017 and March 2018. All patients who underwent TPCT within 3 months of DXA were included. The biomechanical computed tomography (BCT) analysis was performed at a centralized laboratory (O.N. Diagnostics, Berkeley, CA) by 2 trained analysts blinded to the DXA data. DXA-based osteoporosis was defined as a T score ≤-2.5 at the hip or spine. BCT-based osteoporosis was defined as vertebral strength ≤4500 N for women or ≤6500 N for men or trabecular volumetric bone mineral density ≤80 mg/cm³ . Comparative data were available for 91 patients who had complete data for both DXA and BCT: 31 women and 60 men, age 54 ± 11 years (mean ± standard deviation), mean body mass index 28 ± 6 kg/m² . Using DXA as the clinical reference, sensitivity of BCT to detect DXA-defined osteoporosis was 83.3% (20/24 patients) and negative predictive value was 91.7%; specificity and positive predictive value were 65.7% and 46.5%, respectively. BCT analysis of vertebral images on triple-phase computed tomography, routinely obtained during transplant evaluation, can reliably rule out osteoporosis in LT candidates. Patients with suspicion of osteoporosis on TPCT may need further evaluation by DXA.

Risk Assessment of Hip Fracture Based on Machine Learning

Identifying patients with high risk of hip fracture is a great challenge in osteoporosis clinical assessment. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold standard in osteoporosis clinical assessment. However, its classification accuracy is only around 65%. In order to improve this accuracy, this paper proposes the use of Machine Learning (ML) models trained with data from a biomechanical model that simulates a sideways-fall. Machine Learning (ML) models are models able to learn and to make predictions from data. During a training process, ML models learn a function that maps inputs and outputs without previous knowledge of the problem. The main advantage of ML models is that once the mapping function is constructed, they can make predictions for complex biomechanical behaviours in real time. However, despite the increasing popularity of Machine Learning (ML) models and their wide application to many fields of medicine, their use as hip fracture predictors is still limited. This paper proposes the use of ML models to assess and predict hip fracture risk. Clinical, geometric, and biomechanical variables from the finite element simulation of a side fall are used as independent variables to train the models. Among the different tested models, Random Forest stands out, showing its capability to outperform BMD-DXA, achieving an accuracy over 87%, with specificity over 92% and sensitivity over 83%.

Comparison of Vertebral and Femoral Strength Between White and Asian Adults Using Finite Element Analysis of Computed Tomography Scans

Given non-optimal testing rates for dual-energy X-ray absorptiometry (DXA) and the high use of computed tomography (CT) in some Asian countries, biomechanical computed tomography analysis (BCT)-based bone strength testing, which utilizes previously taken clinical CT scans, may improve osteoporosis testing rates. However, an understanding of ethnic differences in such bone strength measurements between Whites and Asians is lacking, which is an obstacle to clinical interpretation. Using previously taken CT and DXA scans, we analyzed bone strength and bone mineral density (BMD) at the hip and spine in two sex- and age-matched community-based cohorts, aged 40 to 80 years: Whites (Rochester, MN, USA) and Koreans (Seoul, South Korea). For both the spine and femur, the age dependence of bone strength was similar for both groups, White (n = 371; women n = 202, 54.5%) and Korean (n = 396; women n = 199, 50.3%). For both sexes, mean spine strength did not differ between groups, but femur strength was 9% to 14% higher in Whites (p ≤ 0.001), an effect that became non-significant after weight adjustment (p = 0.375). For Koreans of both sexes, the fragile bone strength thresholds for classifying osteoporosis, when derived from regional DXA BMD T-score references, equaled the clinically validated thresholds for Whites (in women and men, femoral strength, 3000 N and 3500 N; vertebral strength 4500 N and 6500 N, respectively). Using these thresholds, classifications for osteoporosis for Koreans based on bone strength versus based on DXA BMD T-scores were consistent (89.1% to 94.4% agreement) at both the hip and spine and for both sexes. The BCT-based, clinically validated bone strength thresholds for Whites also applied to Koreans, which may facilitate clinical interpretation of CT-based bone strength measurements for Koreans. © 2020 American Society for Bone and Mineral Research (ASBMR).

Assessment of fracture healing in orthopaedic trauma

Fracture healing is a complex physiologic process, relying on the crucial interplay between biological and mechanical factors. It is generally assessed using imaging modalities, including conventional radiology, CT, MRI and ultrasound (US), based on the fracture and patient features. Although these techniques are routinely used in orthopaedic clinical practice, unfortunately, they do not provide any information about the biomechanical status of the fracture site. Therefore, in recent years, several non-invasive techniques have been proposed to assess bone healing using ultrasonic wave propagation, changes in electrical properties of bones and callus stiffness measurement. Moreover, different research groups are currently developing smart orthopaedic implants (plates, intramedullary nails and external fixators), able to provide information about the fracture healing process. These devices could significantly improve orthopaedic and trauma clinical practice in the future and, at the same time, reduce patients' exposure to X-rays. This study aims to define the role of traditional imaging techniques and emerging technologies in the assessment of the fracture healing process.

Opportunistic Computed Tomography and Spine Surgery: A Narrative Review

STUDY DESIGN

Narrative review.

OBJECTIVE

This article seeks to provide a narrative review regarding the ability of opportunistic information available from computed tomography (CT) scans to guide decisions in spine surgery related to patient bone quality.

METHODS

A review of the literature (limited to human and English language) was performed via PubMed and Google Scholar using the search terms; "osteoporosis" AND "opportunistic" AND "computed tomography" AND "spine surgery." The titles and then abstracts of all identified citations were reviewed for inclusion by 2 of the authors (MS, BAF). Relevant articles were then studied in full text.

RESULTS

A review of the literature found 25 articles that were selected for inclusion in this narrative review. These articles were broadly divided into 4 subcategories: (1) opportunistic CT (oCT) and osteoporosis detection, (2) oCT data and the quality of screw fixation, (3) utilization of Hounsfield units to assess clinical and/or radiographic outcomes following spine fusion, and (4) virtual stress testing in spine surgery.

CONCLUSION

The literature on oCT, as well as associated virtual stress-testing techniques, demonstrate the potential to enhance spine surgery outcomes by preoperatively identifying at-risk patients in need of bone health optimization and informing best techniques for performing spinal fusion surgery on patients with diminished bone quality. While our narrative summary of the limited literature to date suggests a promising future for oCT data, significant additional research and/or radiographic workflow standardization is needed to validate these methods for clinical use.

Stiffness and Strength Predictions From Finite Element Models of the Knee are Associated with Lower-Limb Fractures After Spinal Cord Injury

Spinal cord injury (SCI) is associated with bone fragility and fractures around the knee. The purpose of this investigation was to validate a computed tomography (CT) based finite element (FE) model of the proximal tibia and distal femur under biaxial loading, and to retrospectively quantify the relationship between model predictions and fracture incidence. Twenty-six cadaveric tibiae and femora (n = 13 each) were loaded to 300 N of compression, then internally rotated until failure. FE predictions of torsional stiffness (K) and strength (T_ult) explained 74% (n = 26) and 93% (n = 7) of the variation in experimental measurements, respectively. Univariate analysis and logistic regression were subsequently used to determine if FE predictions and radiographic measurements from CT and dual energy X-ray absorptiometry (DXA) were associated with prevalent lower-limb fracture in 50 individuals with SCI (n = 14 fractures). FE and CT measures, but not DXA, were lower in individuals with fracture. FE predictions of T_ult at the tibia demonstrated the highest odds ratio (4.98; p = 0.006) and receiver operating characteristic (0.84; p = 0.008) but did not significantly outperform other metrics. In conclusion, CT-based FE model predictions were associated with prevalent fracture risk after SCI; this technique could be a powerful tool in future clinical research.

Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning

A great challenge in osteoporosis clinical assessment is identifying patients at higher risk of hip fracture. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold-standard, but its classification accuracy is limited to 65%. DXA-based Finite Element (FE) models have been developed to predict the mechanical failure of the bone. Yet, their contribution has been modest. In this study, supervised machine learning (ML) is applied in conjunction with clinical and computationally driven mechanical attributes. Through this multi-technique approach, we aimed to obtain a predictive model that outperforms BMD and other clinical data alone, as well as to identify the best-learned ML classifier within a group of suitable algorithms. A total number of 137 postmenopausal women (81.4 ± 6.95 years) were included in the study and separated into a fracture group (n = 89) and a control group (n = 48). A semi-automatic and patient-specific DXA-based FE model was used to generate mechanical attributes, describing the geometry, the impact force, bone structure and mechanical response of the bone after a sideways-fall. After preprocessing the whole dataset, 19 attributes were selected as predictors. Support Vector Machine (SVM) with radial basis function (RBF), Logistic Regression, Shallow Neural Networks and Random Forest were tested through a comprehensive validation procedure to compare their predictive performance. Clinical attributes were used alone in another experimental setup for the sake of comparison. SVM was confirmed to generate the best-learned algorithm for both experimental setups, including 19 attributes and only clinical attributes. The first, generated the best-learned model and outperformed BMD by 14pp. The results suggests that this approach could be easily integrated for effective prediction of hip fracture without interrupting the actual clinical workflow.

Hip load capacity and yield load in men and women of all ages

Quantitative computed tomography (QCT) based finite element (FE) models can compute subject-specific proximal femoral strengths, or fracture loads, that are associated with hip fracture risk. These fracture loads are more strongly associated with measured fracture loads than are DXA and QCT measures and are predictive of hip fracture independently of DXA bone mineral density (BMD). However, interpreting FE-computed fracture loads of younger subjects for the purpose of evaluating hip fracture risk in old age is challenging due to limited reference data. The goal of this study was to address this issue by providing reference data for male and female adult subjects of all ages. QCT-based FE models of the left proximal femur of 216 women and 181 men, age 27 to 90 years, from a cohort of Rochester, MN residents were used to compute proximal femoral load capacities, i.e. the maximum loads that can be supported, in single-limb stance and posterolateral fall loading (Stance_LC and Fall_LC, respectively) [US Patent No. 9,245,069] and yield load under fall loading (Fall_yield). To relate these measures to information about hip fracture, the CT scanner and calibration phantom were cross-calibrated with those from our previous prospective study of hip fracture in older fracture and control subjects, the Age Gene/Environment Susceptibility (AGES) Reykjavik cohort. We then plotted Stance_LC_, Fall_LC and Fall_yield versus age for the two cohorts on the same graphs. Thus, proximal femoral strengths in individuals above 70 years of age can be assessed through direct comparison with the FE data from the AGES cohort which were analyzed using identical methods. To evaluate younger individuals, reductions in Stance_LC, Fall_LC and Fall_yield from the time of evaluation to age 70 years can be cautiously estimated from the average yearly cross-sectional decreases found in this study (108 N, 19.4 N and 14.4 N, respectively, in men and 120 N, 19.4 N and 21.6 N, respectively, in women), and the projected fracture loads can be compared with data from the AGES cohort. Although we did not set specific thresholds for identifying individuals at risk of hip fracture, these data provide some guidance and may be used to help establish diagnostic criteria in future. Additionally, given that these data were nearly entirely from Caucasian subjects, future research involving subjects of other races/ethnicities is necessary.

Image-based finite-element modeling of the human femur

Fracture is considered a critical clinical endpoint in skeletal pathologies including osteoporosis and bone metastases. However, current clinical guidelines are limited with respect to identifying cases at high risk of fracture, as they do not account for many mechanical determinants that contribute to bone fracture. Improving fracture risk assessment is an important area of research with clear clinical relevance. Patient-specific numerical musculoskeletal models generated from diagnostic images are widely used in biomechanics research and may provide the foundation for clinical tools used to quantify fracture risk. However, prior to clinical translation, in vitro validation of predictions generated from such numerical models is necessary. Despite adopting radically different models, in vitro validation of image-based finite element (FE) models of the proximal femur (predicting strains and failure loads) have shown very similar, encouraging levels of accuracy. The accuracy of such in vitro models has motivated their application to clinical studies of osteoporotic and metastatic fractures. Such models have demonstrated promising but heterogeneous results, which may be explained by the lack of a uniform strategy with respect to FE modeling of the human femur. This review aims to critically discuss the state of the art of image-based femoral FE modeling strategies, highlighting principal features and differences among current approaches. Quantitative results are also reported with respect to the level of accuracy achieved from in vitro evaluations and clinical applications and are used to motivate the adoption of a standardized approach/workflow for image-based FE modeling of the femur.

A patient-specific numerical model of the ankle joint for the analysis of contact pressure distribution

A patient-specific numerical model of the ankle joint has been developed using open-source software with realistic material properties that mimics the physiological movement of the foot during the stance phase of the gait cycle. The patient-specific ankle geometry has been segmented as a castellated surface using 3DSlicer from the computed tomography image scans of a subject with no congenital or acquired pathology; subsequently, the bones are smoothed, and cartilage is included as a uniform thickness extruded layer. A high-resolution Cartesian mesh has been generated using cfMesh. The material properties are assigned in the model based on the CT image Hounsfield intensities and compared to a sandwich-based material model. Gait data of the same subject was obtained and used to relatively position the tibia, talus, and calcaneus bones in the model. The stance phase of the gait cycle is simulated using a cell-centred finite-volume method implemented in open-source software OpenFOAM. The predicted peak contact pressures occur in the range of 4.85-5.53 MPa with average pressures in the range of 1.56-1.95 MPa, and the contact area ranges between 429 and 707.8 mm² for the entire stance phase with the mid-stance phase predicting the maximum contact area. These predictions are in agreement with results from the literature. The effect of arthritis on the contact characteristics of the ankle joint has also been examined. A concentrated increase in pressure was predicted that could be manifested as pain, thereby leading to reduced motion in the ankle. The model, with continued development, has the capability to understand the effect of joint degradation and furthermore, could help provide a tool to predict the efficiency of therapeutic surgical procedures as well as guide the development of next generation ankle prostheses. The work would be made available in the University College Dublin depository (https://github.com/laxmimurali/anklejoint) as well as research gate once the article has been published.

Biomechanical Computed Tomography analysis (BCT) for clinical assessment of osteoporosis

The surgeon general of the USA defines osteoporosis as "a skeletal disorder characterized by compromised bone strength, predisposing to an increased risk of fracture." Measuring bone strength, Biomechanical Computed Tomography analysis (BCT), namely, finite element analysis of a patient's clinical-resolution computed tomography (CT) scan, is now available in the USA as a Medicare screening benefit for osteoporosis diagnostic testing. Helping to address under-diagnosis of osteoporosis, BCT can be applied "opportunistically" to most existing CT scans that include the spine or hip regions and were previously obtained for an unrelated medical indication. For the BCT test, no modifications are required to standard clinical CT imaging protocols. The analysis provides measurements of bone strength as well as a dual-energy X-ray absorptiometry (DXA)-equivalent bone mineral density (BMD) T-score at the hip and a volumetric BMD of trabecular bone at the spine. Based on both the bone strength and BMD measurements, a physician can identify osteoporosis and assess fracture risk (high, increased, not increased), without needing confirmation by DXA. To help introduce BCT to clinicians and health care professionals, we describe in this review the currently available clinical implementation of the test (VirtuOst), its application for managing patients, and the underlying supporting evidence; we also discuss its main limitations and how its results can be interpreted clinically. Together, this body of evidence supports BCT as an accurate and convenient diagnostic test for osteoporosis in both sexes, particularly when used opportunistically for patients already with CT. Biomechanical Computed Tomography analysis (BCT) uses a patient's CT scan to measure both bone strength and bone mineral density at the hip or spine. Performing at least as well as DXA for both diagnosing osteoporosis and assessing fracture risk, BCT is particularly well-suited to "opportunistic" use for the patient without a recent DXA who is undergoing or has previously undergone CT testing (including hip or spine regions) for an unrelated medical condition.

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.

Perspectives on the non-invasive evaluation of femoral strength in the assessment of hip fracture risk

We reviewed the experimental and clinical evidence that hip bone strength estimated by BMD and/or finite element analysis (FEA) reflects the actual strength of the proximal femur and is associated with hip fracture risk and its changes upon treatment.

INTRODUCTION

The risk of hip fractures increases exponentially with age due to a progressive loss of bone mass, deterioration of bone structure, and increased incidence of falls. Areal bone mineral density (aBMD), measured by dual-energy X-ray absorptiometry (DXA), is the most used surrogate marker of bone strength. However, age-related declines in bone strength exceed those of aBMD, and the majority of fractures occur in those who are not identified as osteoporotic by BMD testing. With hip fracture incidence increasing worldwide, the development of accurate methods to estimate bone strength in vivo would be very useful to predict the risk of hip fracture and to monitor the effects of osteoporosis therapies.

METHODS

We reviewed experimental and clinical evidence regarding the association between aBMD and/orCT-finite element analysis (FEA) estimated femoral strength and hip fracture risk as well as their changes with treatment.

RESULTS

Femoral aBMD and bone strength estimates by CT-FEA explain a large proportion of femoral strength ex vivo and predict hip fracture risk in vivo. Changes in femoral aBMD are strongly associated with anti-fracture efficacy of osteoporosis treatments, though comparable data for FEA are currently not available.

CONCLUSIONS

Hip aBMD and estimated femoral strength are good predictors of fracture risk and could potentially be used as surrogate endpoints for fracture in clinical trials. Further improvements of FEA may be achieved by incorporating trabecular orientations, enhanced cortical modeling, effects of aging on bone tissue ductility, and multiple sideway fall loading conditions.

Effect of Aerobic or Resistance Exercise, or Both, on Bone Mineral Density and Bone Metabolism in Obese Older Adults While Dieting: A Randomized Controlled Trial

Weight loss therapy of older adults with obesity is limited by weight loss-induced decrease in bone mineral density (BMD), which could exacerbate ongoing age-related bone loss and increase the risk for fractures. Therefore, it is recommended that weight loss therapy of older adults with obesity should include an intervention such as regular exercise to reduce the concomitant bone loss. However, the most appropriate exercise types to combine with weight loss therapy in this older population is unknown. In a randomized controlled trial, we performed a head-to-head comparison of aerobic or resistance exercise, or both, during matched ~10% weight loss in 160 older adults with obesity. We measured changes in BMD (total hip, femoral neck, trochanter, intertrochanter, one-third radius, lumbar spine) and bone markers. Changes between groups were analyzed using mixed-model repeated measures analyses of variance. After 6 months of intensive lifestyle interventions, BMD decreased less in the resistance group (-0.006 g/cm² [-0.7%]) and combination group (-0.012 g/cm² [-1.1%]) than in the aerobic group (-0.027 g/cm² [-2.6%]) (p = 0.001 for between-group comparisons). Serum C-telopeptide, procollagen type 1 N-propeptide, and osteocalcin concentrations increased more in the aerobic group (33%, 16%, and 16%, respectively) than in the resistance group (7%, 2%, and 0%, respectively) and combination group (11%, 2%, and 5%, respectively) (p = 0.004 to 0.048 for between-group comparisons). Multiple regression analyses revealed that the decline in whole body mass and serum leptin were the independent predictors of the decline in hip BMD (multiple R = 0.45 [p < .001]). These findings indicate that compared with aerobic exercise, resistance and combined aerobic and resistance exercise are associated with less weight loss-induced decrease in hip BMD and less weight loss-induced increase in bone turnover. Therefore, both resistance and combined aerobic and resistance exercise can be recommended to protect against bone loss during weight loss therapy of older adults with obesity. (LITOE ClinicalTrials.gov number NCT01065636.) © 2019 American Society for Bone and Mineral Research. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.

Artificial neural network analysis of bone quality DXA parameters response to teriparatide in fractured osteoporotic patients

Teriparatide is a bone-forming therapy for osteoporosis that increases bone quantity and texture, with uncertain action on bone geometry. No data are available regarding its influence on bone strain. To investigate teriparatide action on parameters of bone quantity and quality and on Bone Strain Index (BSI), also derived from DXA lumbar scan, based on the mathematical model finite element method. Forty osteoporotic patients with fractures were studied before and after two years of daily subcutaneous 20 mcg of teriparatide with dual X-ray photon absorptiometry to assess bone mineral density (BMD), hip structural analysis (HSA), trabecular bone score (TBS), BSI. Spine deformity index (SDI) was calculated from spine X-ray. Shapiro-Wilks, Wilcoxon and Student's t test were used for classical statistical analysis. Auto Contractive Map was used for Artificial Neural Network Analysis (ANNs). In the entire population, the ameliorations after therapy regarded BSI (-13.9%), TBS (5.08%), BMD (8.36%). HSA parameters of femoral shaft showed a worsening. Dividing patients into responders (BMD increase >10%) and non-responders, the first presented TBS and BSI ameliorations (11.87% and -25.46%, respectively). Non-responders presented an amelioration of BSI only, but less than in the other subgroup (-6.57%). ANNs maps reflect the mentioned bone quality improvements. Teriparatide appears to ameliorate not only BMD and TBS, but also BSI, suggesting an increase of bone strength that may explain the known reduction in fracture risk, not simply justified by BMD increase. BSI appears to be a sensitive index of TPD effect. ANNs appears to be a valid tool to investigate complex clinical systems.

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.

Prediction of lumbar vertebral strength of elderly men based on quantitative computed tomography images using machine learning

The parameters extracted from quantitative computed tomography (QCT) images were used to predict vertebral strength through machine learning models, and the highly accurate prediction indicated that it may be a promising approach to assess fracture risk in clinics.

INTRODUCTION

Vertebral fracture is common in elderly populations. The main factor contributing to vertebral fracture is the reduced vertebral strength. This study aimed to predict vertebral strength based on clinical QCT images by using machine learning.

METHODS

Eighty subjects with QCT data of lumbar spine were randomly selected from the MrOS cohorts. L1 vertebral strengths were computed by QCT-based finite element analysis. A total of 58 features of each L1 vertebral body were extracted from QCT images, including grayscale distribution, grayscale values of 39 partitioned regions, BMD_QCT, structural rigidity, axial rigidity, and BMD_QCTA_min. Feature selection and dimensionality reduction were used to simplify the 58 features. General regression neural network and support vector regression models were developed to predict vertebral strength. Performance of prediction models was quantified by the mean squared error, the coefficient of determination, the mean bias, and the SD of bias.

RESULTS

The 58 parameters were simplified to five features (grayscale value of the 60% percentile, grayscale values of three specific partitioned regions, and BMD_QCTA_min) and nine principal components (PCs). High accuracy was achieved by using the five features or the nine PCs to predict vertebral strength.

CONCLUSIONS

This study provided an effective approach to predict vertebral strength and showed that it may have great potential in clinical applications for noninvasive assessment of vertebral fracture risk.