Are CT-Based Finite Element Model Predictions of Femoral Bone Strength Clinically Useful?

Phantomless calibration of CT scans for hip fracture risk prediction in silico: Comparison with phantom-based calibration

Finite element models built from quantitative computed tomography images rely on element-wise mapping of material properties starting from Hounsfield Units (HU), which can be converted into mineral densities upon calibration. While calibration is preferably carried out by scanning a phantom with known-density components, conducting phantom-based calibration may not always be possible. In such cases, a phantomless procedure, where the scanned subject's tissues are used as a phantom, is an interesting alternative. The aim of this study was to compare a phantom-based and a phantomless calibration method on 41 postmenopausal women. The proposed phantomless calibration utilized air, adipose, and muscle tissues, with reference equivalent mineral density values of -797, -95, and 38 mg/cm3, extracted from a previously performed phantom-based calibration. A 9-slice volume of interest (VOI) centred between the femoral head and knee rotation centres was chosen. Reference HU values for air, adipose, and muscle tissues were extracted by identifying HU distribution peaks within the VOI, and patient-specific calibration was performed using linear regression. Comparison of FE models calibrated with the two methods showed average relative differences of 1.99% for Young's modulus1.30% for tensile and 1.34% for compressive principal strains. Excellent correlations (R2 > 0.99) were identified for superficial maximum tensile and minimum compressive strains. Maximum normalised root mean square relative error (RMSRE) values settled at 4.02% for Young's modulus, 2.99% for tensile, and 3.22% for compressive principal strains, respectively. The good agreement found between the two methods supports the adoption of the proposed methodology when phantomless calibration is needed.

Quantitative Skeletal Imaging and Image-Based Modeling in Pediatric Orthopaedics

PURPOSE OF REVIEW

Musculoskeletal imaging serves a critical role in clinical care and orthopaedic research. Image-based modeling is also gaining traction as a useful tool in understanding skeletal morphology and mechanics. However, there are fewer studies on advanced imaging and modeling in pediatric populations. The purpose of this review is to provide an overview of recent literature on skeletal imaging modalities and modeling techniques with a special emphasis on current and future uses in pediatric research and clinical care.

RECENT FINDINGS

While many principles of imaging and 3D modeling are relevant across the lifespan, there are special considerations for pediatric musculoskeletal imaging and fewer studies of 3D skeletal modeling in pediatric populations. Improved understanding of bone morphology and growth during childhood in healthy and pathologic patients may provide new insight into the pathophysiology of pediatric-onset skeletal diseases and the biomechanics of bone development. Clinical translation of 3D modeling tools developed in orthopaedic research is limited by the requirement for manual image segmentation and the resources needed for segmentation, modeling, and analysis. This paper highlights the current and future uses of common musculoskeletal imaging modalities and 3D modeling techniques in pediatric orthopaedic clinical care and research.

Anisotropy, Anatomical Region, and Additional Variables Influence Young's Modulus of Bone: A Systematic Review and Meta-Analysis

The importance of finite element analysis (FEA) is growing in orthopedic research, especially in implant design. However, Young's modulus (E) values, one of the most fundamental parameters, can range across a wide scale. Therefore, our study aimed to identify factors influencing E values in human bone specimens. We report our systematic review and meta-analysis based on the recommendation of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline. We conducted the analysis on November 21, 2021. We included studies investigating healthy human bone specimens and reported on E values regarding demographic data, specimen characteristics, and measurement specifics. In addition, we included study types reporting individual specimen measurements. From the acquired data, we created a cohort in which we performed an exploratory data analysis that included the explanatory variables selected by random forest and regression trees methods, and the comparison of groups using independent samples Welch's t test. A total of 756 entries were included from 48 articles. Eleven different bones of the human body were included in these articles. The range of E values is between 0.008 and 33.7 GPa. The E values were most heavily influenced by the cortical or cancellous type of bone tested. Measuring method (compression, tension, bending, and nanoindentation), the anatomical region within a bone, the position of the bone within the skeleton, and the bone specimen size had a decreasing impact on the E values. Bone anisotropy, specimen condition, patient age, and sex were selected as important variables considering the value of E. On the basis of our results, E values of a bone change with bone characteristics, measurement techniques, and demographic variables. Therefore, the evaluation of FEA should be performed after the standardization of in vitro measurement protocol. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

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).

Cost-Effectiveness Analysis of CT-Based Finite Element Modeling for Osteoporosis Screening in Secondary Fracture Prevention: An Early Health Technology Assessment in the Netherlands

Objective. To conduct cost-utility analyses for Computed Tomography To Strength (CT2S), a novel osteoporosis screening service, compared with dual-energy X-ray absorptiometry (DXA), treat all without screening, and no screening methods for Dutch postmenopausal women referred to fracture liaison service (FLS). CT2S uses CT scans to generate femur models and simulate sideways fall scenarios for bone strength assessment. Methods. Early health technology assessment (HTA) was adopted to evaluate CT2S as a novel osteoporosis screening tool for secondary fracture prevention. We constructed a 2-dimensional simulation model considering 4 strategies (no screening, treat all without screening, DXA, CT2S) together with screening intervals (5 y, 2 y), treatments (oral alendronate, zoledronic acid), and discount rate scenarios among Dutch women in 3 age groups (60s, 70s, and 80s). Strategy comparisons were based on incremental cost-effectiveness ratios (ICERs), considering an ICER below €20,000 per QALY gained as cost-effective in the Netherlands. Results. Under the base-case scenario, CT2S versus DXA had estimated ICERs of €41,200 and €14,083 per QALY gained for the 60s and 70s age groups, respectively. For the 80s age group, CT2S was more effective and less costly than DXA. Changing treatment from weekly oral alendronate to annual zoledronic acid substantially decreased CT2S versus DXA ICERs across all age groups. Setting the screening interval to 2 y increased CT2S versus DXA ICERs to €100,333, €55,571, and €15,750 per QALY gained for the 60s, 70s, and 80s age groups, respectively. In all simulated populations and scenarios, CT2S was cost-effective (in some cases dominant) compared with the treat all strategy and cost-saving (more effective and less costly) compared with no screening. Conclusion. CT2S was estimated to be potentially cost-effective in the 70s and 80s age groups considering the willingness-to-pay threshold of the Netherlands. This early HTA suggests CT2S as a potential novel osteoporosis screening tool for secondary fracture prevention.

Highlights

For postmenopausal Dutch women who have been referred to the FLS, direct access to CT2S may be cost-effective compared with DXA for age groups 70s and 80s, when considering the ICER threshold of the Netherlands. This study positions CT2S as a potential novel osteoporosis-screening tool for secondary fracture prevention in the clinical setting.A shorter screening interval of 2 y increases the effectiveness of both screening strategies, but the ICER of CT2S compared with DXA also increased substantially, which made CT2S no longer cost-effective for the 70s age group; however, it remains cost-effective for individuals in their 80s.Annual zoledronic acid treatment with better adherence may contribute to a lower cost-effectiveness ratio when comparing CT2S to DXA screening and the treat all strategies for all age groups.

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

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

The clinician's guide to prevention and treatment of osteoporosis

Osteoporosis is the most common metabolic bone disease in the USA and the world. It is a subclinical condition until complicated by fracture(s). These fractures place an enormous medical and personal burden on individuals who suffer from them and take a significant economic toll. Any new fracture in an adult aged 50 years or older signifies imminent elevated risk for subsequent fractures, particularly in the year following the initial fracture. What a patient perceives as an unfortunate accident may be seen as a sentinel event indicative of bone fragility and increased future fracture risk even when the result of considerable trauma. Clinical or subclinical vertebral fractures, the most common type of osteoporotic fractures, are associated with a 5-fold increased risk for additional vertebral fractures and a 2- to 3-fold increased risk for fractures at other sites. Untreated osteoporosis can lead to a vicious cycle of recurrent fracture(s), often resulting in disability and premature death. In appropriate patients, treatment with effective antifracture medication prevents fractures and improves outcomes. Primary care providers and medical specialists are critical gatekeepers who can identify fractures and initiate proven osteoporosis interventions. Osteoporosis detection, diagnosis, and treatment should be routine practice in all adult healthcare settings. The Bone Health and Osteoporosis Foundation (BHOF) - formerly the National Osteoporosis Foundation - first published the Clinician's Guide in 1999 to provide accurate information on osteoporosis prevention and treatment. Since that time, significant improvements have been made in diagnostic technologies and treatments for osteoporosis. Despite these advances, a disturbing gap persists in patient care. At-risk patients are often not screened to establish fracture probability and not educated about fracture prevention. Most concerning, the majority of highest risk women and men who have a fracture(s) are not diagnosed and do not receive effective, FDA-approved therapies. Even those prescribed appropriate therapy are unlikely to take the medication as prescribed. The Clinician's Guide offers concise recommendations regarding prevention, risk assessment, diagnosis, and treatment of osteoporosis in postmenopausal women and men aged 50 years and older. It includes indications for bone densitometry as well as fracture risk thresholds for pharmacologic intervention. Current medications build bone and/or decrease bone breakdown and dramatically reduce incident fractures. All antifracture therapeutics treat but do not cure the disease. Skeletal deterioration resumes sooner or later when a medication is discontinued-sooner for nonbisphosphonates and later for bisphosphonates. Even if normal BMD is achieved, osteoporosis and elevated risk for fracture are still present. The diagnosis of osteoporosis persists even if subsequent DXA T-scores are above - 2.5. Ongoing monitoring and strategic interventions will be necessary if fractures are to be avoided. In addition to pharmacotherapy, adequate intake of calcium and vitamin D, avoidance of smoking and excessive alcohol intake, weight-bearing and resistance-training exercise, and fall prevention are included in the fracture prevention armamentarium. Where possible, recommendations in this guide are based on evidence from RCTs; however, relevant published data and guidance from expert clinical experience provides the basis for recommendations in those areas where RCT evidence is currently deficient or not applicable to the many osteoporosis patients not considered for RCT participation due to age and morbidity.

Osteoporosis in 10 years time: a glimpse into the future of osteoporosis

Patients living with osteoporosis are projected to increase dramatically in the next decade. Alongside the forecasted increased societal and economic burden, we will live a crisis of fractures. However, we will have novel pharmacological treatment to face this crisis and, more importantly, new optimized treatment strategies. Fracture liaison services will be probably implemented on a large scale worldwide, helping to prevent additional fractures in high-risk patients. In the next decade, novel advances in the diagnostic tools will be largely available. Moreover, new and more precise fracture risk assessment tools will change our ability to detect patients at high risk of fractures. Finally, big data and artificial intelligence will help us to move forward into the world of precision medicine. In the present review, we will discuss the future epidemiology and costs of osteoporosis, the advances in early and accurate diagnosis of osteoporosis, with a special focus on biomarkers and imaging tools. Then we will examine new and refined fracture risk assessment tools, the role of fracture liaison services, and a future perspective on osteoporosis treatment.

Personalised 3D Assessment of Trochanteric Soft Tissues Improves HIP Fracture Classification Accuracy

Passive soft tissues surrounding the trochanteric region attenuate fall impact forces and thereby control hip fracture risk. The degree of attenuation is related to Soft Tissue Thickness (STT). STT at the neutral hip impact orientation, estimated using a regression relation in body mass index (BMI), was previously shown to influence the current absolute risk of hip fracture (ARF0) and its fracture classification accuracy. The present study investigates whether fracture classification using ARF0 improves when STT is determined from the subject’s Computed-Tomography (CT) scans (i.e. personalised) in an orientation-specific (i.e. 3D) manner. STT is calculated as the shortest distance along any impact orientation between a semi-automatically segmented femur surface and an automatically segmented soft tissue/air boundary. For any subject, STT along any of the 33 impact orientations analysed always exceeds the value estimated using BMI. Accuracy of fracture classification using ARF0 improves when using personalised 3D STT estimates (AUC = 0.87) instead of the BMI-based STT estimate (AUC = 0.85). The improvement is smaller (AUC = 0.86) when orientation-specificity of CT-based STT is suppressed and is nil when personalisation is suppressed instead. Thus, fracture classification using ARF0 improves when CT is used to personalise STT estimates and improves further when, in addition, the estimates are orientation specific.

Improving the Hip Fracture Risk Prediction with a Statistical Shape-and-Intensity Model of the Proximal Femur

Severe predictions have been made regarding osteoporotic fracture incidence for the next years, with major economic and social impacts in a worldwide greying society. However, the performance of the currently adopted gold standard for fracture risk prediction, the areal Bone Mineral Density (aBMD), remains moderate. To overcome current limitations, the construction of statistical models of the proximal femur, based on three-dimensional shape and intensity (a hallmark of bone density), is here proposed for predicting hip fracture in a Caucasian postmenopausal cohort. Partial Least Square (PLS)-based statistical models of the shape, intensity and their combination were developed, and the corresponding modes and components were identified. Logistic regression models using the first two shape, intensity and shape-intensity PLS components were implemented and tested within a 10-fold cross-validation procedure as predictors of hip fracture. It emerged that (1) intensity components were superior to shape components in stratifying patients according to their fracture status, and that (2) a combination of intensity and shape improved patients risk stratification. The area under the ROC curve was 0.64, 0.85 and 0.92 for the models based on shape, intensity and shape-intensity combination respectively, against a 0.72 value for the aBMD standard approach. Based on these findings, the presented methodology turns out to be promising in tackling the need for an enhanced fracture risk assessment.

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.

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.

Delivering computationally-intensive digital patient applications to the clinic: An exemplar solution to predict femoral bone strength from CT data

BACKGROUND AND OBJECTIVE

Whilst fragility hip fractures commonly affect elderly people, often causing permanent disability or death, they are rarely addressed in advance through preventive techniques. Quantification of bone strength can help to identify subjects at risk, thus reducing the incidence of fractures in the population. In recent years, researchers have shown that finite element models (FEMs) of the hip joint, derived from computed tomography (CT) images, can predict bone strength more accurately than other techniques currently used in the clinic. The specialised hardware and trained personnel required to perform such analyses, however, limits the widespread adoption of FEMs in clinical contexts. In this manuscript we present CT2S (Computed Tomography To Strength), a system developed in collaboration between The University of Sheffield and Sheffield Teaching Hospitals, designed to streamline access to this complex workflow for clinical end-users.

METHODS

The system relies on XNAT and makes use of custom apps based on open source software. Available through a website, it allows doctors in the healthcare environment to benefit from FE based bone strength estimation without being exposed to the technical aspects, which are concealed behind a user-friendly interface. Clinicians request the analysis of CT scans of a patient through the website. Using XNAT functionality, the anonymised images are automatically transferred to the University research facility, where an operator processes them and estimates the bone strength through FEM using a combination of open source and commercial software. Following the analysis, the doctor is provided with the results in a structured report.

RESULTS

The platform, currently available for research purposes, has been deployed and fully tested in Sheffield, UK. The entire analysis requires processing times ranging from 3.5 to 8 h, depending on the available computational power.

CONCLUSIONS

The short processing time makes the system compatible with current clinical workflows. The use of open source software and the accurate description of the workflow given here facilitates the deployment in other centres.

Opportunistic diagnosis of osteoporosis, fragile bone strength and vertebral fractures from routine CT scans; a review of approved technology systems and pathways to implementation

Osteoporosis causes bones to become weak, porous and fracture more easily. While a vertebral fracture is the archetypal fracture of osteoporosis, it is also the most difficult to diagnose clinically. Patients often suffer further spine or other fractures, deformity, height loss and pain before diagnosis. There were an estimated 520,000 fragility fractures in the United Kingdom (UK) in 2017 (costing £4.5 billion), a figure set to increase 30% by 2030. One way to improve both vertebral fracture identification and the diagnosis of osteoporosis is to assess a patient's spine or hips during routine computed tomography (CT) scans. Patients attend routine CT for diagnosis and monitoring of various medical conditions, but the skeleton can be overlooked as radiologists concentrate on the primary reason for scanning. More than half a million CT scans done each year in the National Health Service (NHS) could potentially be screened for osteoporosis (increasing 5% annually). If CT-based screening became embedded in practice, then the technique could have a positive clinical impact in the identification of fragility fracture and/or low bone density. Several companies have developed software methods to diagnose osteoporosis/fragile bone strength and/or identify vertebral fractures in CT datasets, using various methods that include image processing, computational modelling, artificial intelligence and biomechanical engineering concepts. Technology to evaluate Hounsfield units is used to calculate bone density, but not necessarily bone strength. In this rapid evidence review, we summarise the current literature underpinning approved technologies for opportunistic screening of routine CT images to identify fractures, bone density or strength information. We highlight how other new software technologies have become embedded in NHS clinical practice (having overcome barriers to implementation) and highlight how the novel osteoporosis technologies could follow suit. We define the key unanswered questions where further research is needed to enable the adoption of these technologies for maximal patient benefit.

Femoral strength can be predicted from 2D projections using a 3D statistical deformation and texture model with finite element analysis

Ultimate force of the proximal human femur can be predicted using Finite Element Analysis (FEA), but the models rely on 3D computed tomography images. Landmark-based statistical appearance models (SAM) and B-Spline transformation-based statistical deformation models (SDM) have been used to estimate 3D images from 2D projections, which facilitates model generation and reduces the radiation dose. However, there is no literature on the accuracy of SDM-based FEA models of bones with respect to experimental results. In this study, a methodology for an enhanced SDM with textural information is presented. The statistical deformation and texture models (SDTMs) are based on a set of 37 quantitative CT (QCT) images. They were used to estimate 3D images from two or one projections of the set in a leave-one-out setup. These estimations where then used to create FEA models. The ultimate force predicted by FEA models estimated from two or one projection using the SDTMs were compared to the experimental ultimate force from a previous study on the same femora and to the results of standard QCT-based FEA models. High correlations between predictions and experimental measurements were found for FEA models reconstructed from 2D projections with R²=0.835 when based on two projections and R²=0.724 when using one projection. The correlations were comparable to those reached with standard QCT-based FE-models with the experimental results (R²=0.795). This study shows the high potential of SDTM-based 3D image reconstruction and FEA modelling from 2D projections to predict femoral ultimate force.

A systematic approach to the scale separation problem in the development of multiscale models

Throughout engineering there are problems where it is required to predict a quantity based on the measurement of another, but where the two quantities possess characteristic variations over vastly different ranges of time and space. Among the many challenges posed by such 'multiscale' problems, that of defining a 'scale' remains poorly addressed. This fundamental problem has led to much confusion in the field of biomedical engineering in particular. The present study proposes a definition of scale based on measurement limitations of existing instruments, available computational power, and on the ranges of time and space over which quantities of interest vary characteristically. The definition is used to construct a multiscale modelling methodology from start to finish, beginning with a description of the system (portion of reality of interest) and ending with an algorithmic orchestration of mathematical models at different scales within the system. The methodology is illustrated for a specific but well-researched problem. The concept of scale and the multiscale modelling approach introduced are shown to be easily adaptable to other closely related problems. Although out of the scope of this paper, we believe that the proposed methodology can be applied widely throughout engineering.

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.

Beyond Bone Mineral Density: A New Dual X-Ray Absorptiometry Index of Bone Strength to Predict Fragility Fractures, the Bone Strain Index

For a proper assessment of osteoporotic fragility fracture prediction, all aspects regarding bone mineral density, bone texture, geometry and information about strength are necessary, particularly in endocrinological and rheumatological diseases, where bone quality impairment is relevant. Data regarding bone quantity (density) and, partially, bone quality (structure and geometry) are obtained by the gold standard method of dual X-ray absorptiometry (DXA). Data about bone strength are not yet readily available. To evaluate bone resistance to strain, a new DXA-derived index based on the Finite Element Analysis (FEA) of a greyscale of density distribution measured on spine and femoral scan, namely Bone Strain Index (BSI), has recently been developed. Bone Strain Index includes local information on density distribution, bone geometry and loadings and it differs from bone mineral density (BMD) and other variables of bone quality like trabecular bone score (TBS), which are all based on the quantification of bone mass and distribution averaged over the scanned region. This state of the art review illustrates the methodology of BSI calculation, the findings of its in reproducibility and the preliminary data about its capability to predict fragility fracture and to monitor the follow up of the pharmacological treatment for osteoporosis.

A Review on Recent Advances in the Constitutive Modeling of Bone Tissue

PURPOSE OF REVIEW

Image-based finite element analysis (FEA) to predict and understand the biomechanical response has become an essential methodology in musculoskeletal research. An important part of such simulation models is the constitutive material model of which recent advances are summarized in this review.

RECENT FINDINGS

The review shows that existing models from other fields were introduced, such as cohesion zone (cortical bone) or phase-field models (trabecular bone). Some progress has been made in describing cortical bone involving physical mechanisms such as microcracks. Problems with validations at different length scales remain a problem. The improvement of recent constitutive models is partially obscured by uncertainties that affect overall predictions, such as image quality and calibration or boundary conditions. Nevertheless, in vivo CT-based FEA simulations based on a sophisticated constitutive behavior are a very valuable tool for clinical-related osteoporosis research.

Body Anthropometry and Bone Strength Conjointly Determine the Risk of Hip Fracture in a Sideways Fall

We hypothesize that variations of body anthropometry, conjointly with the bone strength, determine the risk of hip fracture. To test the hypothesis, we compared, in a simulated sideways fall, the hip impact energy to the energy needed to fracture the femur. Ten femurs from elderly donors were tested using a novel drop-tower protocol for replicating the hip fracture dynamics during a fall on the side. The impact energy was varied for each femur according to the donor’s body weight, height and soft-tissue thickness, by adjusting the drop height and mass. The fracture pattern, force, energy, strain in the superior femoral neck, bone morphology and microarchitecture were evaluated. Fracture patterns were consistent with clinically relevant hip fractures, and the superior neck strains and timings were comparable with the literature. The hip impact energy (11 – 95 J) and the fracture energy (11 – 39 J) ranges overlapped and showed comparable variance (CV = 69 and 61%, respectively). The aBMD-based definition of osteoporosis correctly classified 7 (70%) fracture/non-fracture cases. The incorrectly classified cases presented large impact energy variations, morphology variations and large subcortical voids as seen in microcomputed tomography. In conclusion, the risk of osteoporotic hip fracture in a sideways fall depends on both body anthropometry and bone strength.

Combining shape and intensity dxa-based statistical approaches for osteoporotic HIP fracture risk assessment

Aiming to improve osteoporotic hip fracture risk detection, factors other than the largely adopted Bone Mineral Density (BMD) have been investigated as potential risk predictors. In particular Hip Structural Analysis (HSA)-derived parameters accounting for femur geometry, extracted from Dual-energy X-ray Absorptiometry (DXA) images, have been largely considered as geometric risk factors. However, HSA-derived parameters represent discrete and cross-correlated quantities, unable to describe proximal femur geometry as a whole and tightly related to BMD. Focusing on a post-menopausal cohort (N = 28), in this study statistical models of bone shape and BMD distribution have been developed to investigate their possible role in fracture risk. Due to unavailable retrospective patient-specific fracture risk information, here a surrogate fracture risk based on 3D computer simulations has been employed for the statistical framework construction. When considered separately, BMD distribution performed better than shape in explaining the surrogate fracture risk variability for the analysed cohort. However, the combination of BMD and femur shape quantities in a unique statistical model yielded better results. In detail, the first shape-intensity combined mode identified using a Partial Least Square (PLS) algorithm was able to explain 70% of the surrogate fracture risk variability, thus suggesting that a more effective patients stratification can be obtained applying a shape-intensity combination approach, compared to T-score. The findings of this study strongly advocate future research on the role of a combined shape-BMD statistical framework in fracture risk determination.

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.

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.

Microindentation: A New Technique for Bone Quality Assessment

Fractures are the result of the application of a greater force on bone than its strength. Therefore, to understand fracture physiopathology, it is essential to know bone strength determinants. These include bone mineral density (BMD), bone spatial structure (bone geometry and microarchitecture) and bone mechanical and tissue properties. While BMD and bone spatial structure can be easily evaluated through imaging technology, assessment of bone tissue and mechanical properties is complex and typically requires invasive techniques that are not suitable in clinical practice. Microindentation is a relatively recently developed technique that directly measures bone tissue and mechanical properties in patients in a fast, safe, feasible and minimally invasive way. It appears to be particularly informative in diseases associated with an increased risk of fracture not explained by BMD values as occurs in X-linked hypophosphataemia (XLH). The aim of this article is to provide an overview on bone microindentation and its potential utility in the evaluation of patients with XLH.

Predicting bone strength from CT data: Clinical applications

In this review we summarise over 15 years of research and development around the prediction of whole bones strength from Computed Tomography data, with particular reference to the prediction of the risk of hip fracture in osteoporotic patients. We briefly discuss the theoretical background, and then provide a summary of the laboratory and clinical validation of these modelling technologies. We then discuss the three current clinical applications: in clinical research, in clinical trials, and in clinical practice. On average the strength predicted with finite element models (QCT-FE) based on computed tomography is 7% more accurate that that predicted with areal bone mineral density from Dual X-ray Absorptiometry (DXA-aBMD), the current standard of care, both in term of laboratory validation on cadaver bones and in terms of stratification accuracy on clinical cohorts of fractured and non-fractured women. This improved accuracy makes QCT-FE superior to DXA-aBMD in clinical research and in clinical trials, where the its use can cut in half the number of patients to be enrolled to get the same statistical power. For routine clinical use to decide who to treat with antiresorptive drugs, QCT-FE is more accurate but less cost-effective than DXA-aBMD, at least when the decision is on first line treatment like bisphosphonates. But the ability to predict skeletal strength from medical imaging is now opening a number of other applications, for example in paediatrics and oncology.

Radiofrequency echographic multi-spectrometry for the in-vivo assessment of bone strength: state of the art-outcomes of an expert consensus meeting organized by the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO)

PURPOSE

The purpose of this paper was to review the available approaches for bone strength assessment, osteoporosis diagnosis and fracture risk prediction, and to provide insights into radiofrequency echographic multi spectrometry (REMS), a non-ionizing axial skeleton technique.

METHODS

A working group convened by the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis met to review the current image-based methods for bone strength assessment and fracture risk estimation, and to discuss the clinical perspectives of REMS.

RESULTS

Areal bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA) is the consolidated indicator for osteoporosis diagnosis and fracture risk assessment. A more reliable fracture risk estimation would actually require an improved assessment of bone strength, integrating also bone quality information. Several different approaches have been proposed, including additional DXA-based parameters, quantitative computed tomography, and quantitative ultrasound. Although each of them showed a somewhat improved clinical performance, none satisfied all the requirements for a widespread routine employment, which was typically hindered by unclear clinical usefulness, radiation doses, limited accessibility, or inapplicability to spine and hip, therefore leaving several clinical needs still unmet. REMS is a clinically available technology for osteoporosis diagnosis and fracture risk assessment through the estimation of BMD on the axial skeleton reference sites. Its automatic processing of unfiltered ultrasound signals provides accurate BMD values in view of fracture risk assessment.

CONCLUSIONS

New approaches for improved bone strength and fracture risk estimations are needed for a better management of osteoporotic patients. In this context, REMS represents a valuable approach for osteoporosis diagnosis and fracture risk prediction.

The effect of boundary and loading conditions on patient classification using finite element predicted risk of fracture

BACKGROUND

Osteoporotic proximal femoral fractures associated to falls are a major health burden in the ageing society. Recently, bone strength estimated by finite element models emerged as a feasible alternative to areal bone mineral density as a predictor of fracture risk. However, previous studies showed that the accuracy of patients' classification under their risk of fracture using finite element strength when simulating posterolateral falls is only marginally better than that of areal bone mineral density. Patients tend to fall in various directions: since the predicted strength is sensitive to the fall direction, a prediction based on certain fall directions might not be fully representative of the physical event. Hence, side fall boundary conditions may not be completely representing the physical event.

METHODS

The effect of different side fall boundary and loading conditions on a retrospective cohort of 98 postmenopausal women was evaluated to test models' ability to discriminate fracture and control cases. Three different boundary conditions (Linear, Multi-point constraints and Contact model) were investigated under various anterolateral and posterolateral falls.

FINDINGS

The stratification power estimated by the area under the receiver operating characteristic curve was highest for Contact model (0.82), followed by Multi-point constraints and Linear models with 0.80. Both Contact and MPC models predicted high strains in various locations of the proximal femur including the greater trochanter, which has rarely reported previously.

INTERPRETATION

A full range of fall directions and less restrictive displacement constraints can improve the finite element strength ability to classify patients under their risk of fracture.

Cost-Effectiveness of Osteoporosis Screening Using Biomechanical Computed Tomography for Patients With a Previous Abdominal CT

Osteoporosis screening rates by DXA are low (9.5% women, 1.7% men) in the US Medicare population aged 65 years and older. Addressing this care gap, we estimated the benefits of a validated osteoporosis diagnostic test suitable for patients age 65 years and older with an abdominal computed tomography (CT) scan taken for any indication but without a recent DXA. Our analysis assessed a hypothetical cohort of 1000 such patients in a given year, and followed them for 5 years. Separately for each sex, we used Markov modeling to compare two mutually exclusive scenarios: (i) utilizing the CT scans, perform one-time "biomechanical computed tomography" (BCT) analysis to identify high-risk patients on the basis of both femoral strength and hip BMD T-scores; (ii) ignore the CT scan, and rely instead on usual care, consisting of future annual DXA screening at typical Medicare rates. For patients with findings indicative of osteoporosis, 50% underwent 2 years of treatment with alendronate. We found that BCT provided greater clinical benefit at lower cost for both sexes than usual care. In our base case, compared to usual care, BCT prevented hip fractures over a 5-year window (3.1 per 1000 women; 1.9 per 1000 men) and increased quality-adjusted life years (2.95 per 1000 women; 1.48 per 1000 men). Efficacy and savings increased further for higher-risk patient pools, greater treatment adherence, and longer treatment duration. When the sensitivity and specificity of BCT were set to those for DXA, the prevented hip fractures versus usual care remained high (2.7 per 1000 women; 1.5 per 1000 men), indicating the importance of high screening rates on clinical efficacy. Therefore, for patients with a previously taken abdominal CT and without a recent DXA, osteoporosis screening using biomechanical computed tomography may be a cost-effective alternative to current usual care. © 2019 American Society for Bone and Mineral Research.

Neuro-musculoskeletal flexible multibody simulation yields a framework for efficient bone failure risk assessment

Fragility fractures are a major socioeconomic problem. A non-invasive, computationally-efficient method for the identification of fracture risk scenarios under the representation of neuro-musculoskeletal dynamics does not exist. We introduce a computational workflow that integrates modally-reduced, quantitative CT-based finite-element models into neuro-musculoskeletal flexible multibody simulation (NfMBS) for early bone fracture risk assessment. Our workflow quantifies the bone strength via the osteogenic stresses and strains that arise due to the physiological-like loading of the bone under the representation of patient-specific neuro-musculoskeletal dynamics. This allows for non-invasive, computationally-efficient dynamic analysis over the enormous parameter space of fracture risk scenarios, while requiring only sparse clinical data. Experimental validation on a fresh human femur specimen together with femur strength computations that were consistent with literature findings provide confidence in the workflow: The simulation of an entire squat took only 38 s CPU-time. Owing to the loss (16% cortical, 33% trabecular) of bone mineral density (BMD), the strain measure that is associated with bone fracture increased by 31.4%; and yielded an elevated risk of a femoral hip fracture. Our novel workflow could offer clinicians with decision-making guidance by enabling the first combined in-silico analysis tool using NfMBS and BMD measurements for optimized bone fracture risk assessment.

A multiscale model to predict current absolute risk of femoral fracture in a postmenopausal population

Osteoporotic hip fractures are a major healthcare problem. Fall severity and bone strength are important risk factors of hip fracture. This study aims to obtain a mechanistic explanation for fracture risk in dependence of these risk factors. A novel modelling approach is developed that combines models at different scales to overcome the challenge of a large space-time domain of interest and considers the variability of impact forces between potential falls in a subject. The multiscale model and its component models are verified with respect to numerical approximations made therein, the propagation of measurement uncertainties of model inputs is quantified, and model predictions are validated against experimental and clinical data. The main results are model predicted absolute risk of current fracture (ARF0) that ranged from 1.93 to 81.6% (median 36.1%) for subjects in a retrospective cohort of 98 postmenopausal British women (49 fracture cases and 49 controls); ARF0 was computed up to a precision of 1.92 percentage points (pp) due to numerical approximations made in the model; ARF0 possessed an uncertainty of 4.00 pp due to uncertainties in measuring model inputs; ARF0 classified observed fracture status in the above cohort with AUC = 0.852 (95% CI 0.753-0.918), 77.6% specificity (95% CI 63.4-86.5%) and 81.6% sensitivity (95% CI 68.3-91.1%). These results demonstrate that ARF0 can be computed using the model with sufficient precision to distinguish between subjects and that the novel mechanism of fracture risk determination based on fall dynamics, hip impact and bone strength can be considered validated.

From bed to bench: How in silico medicine can help ageing research

Driven by the raising ethical concerns surrounding animal experimentation, there is a growing interest for non-animal methods, in vitro or in silico technologies that can be used to reduce, refine, and replace animal experimentation. In addition, animal experimentation is being critically revised in regard to its ability to predict clinical outcomes. In this manuscript we describe an initial exploration where a set of in vivo imaging based subject-specific technologies originally developed to predict the risk of femoral strength and hip fracture in osteoporotic patients, were adapted to assess the efficacy of bone drugs pre-clinically on mice. The CT2S technology we developed generates subject-specific models based on Computed Tomography that can separate fractured and non-fractured patients with an accuracy of 82%. When used in mouse experiments the use of in vivo imaging and modelling was found to improve the reproducibility of Bone Mineral Content measurements to a point where up to 63% less mice would be required to achieve the same statistical power of a conventional cross-sectional study. We also speculate about a possible approach where animal-specific and patient-specific models could be used to better translate the observation made on animal models into predictions of response in humans.