Conventional finite element models estimate the strength of metastatic human vertebrae despite alterations of the bone's tissue and structure

Finite element models with automatic computed tomography bone segmentation for failure load computation

Bone segmentation is an important step to perform biomechanical failure load simulations on in-vivo CT data of patients with bone metastasis, as it is a mandatory operation to obtain meshes needed for numerical simulations. Segmentation can be a tedious and time consuming task when done manually, and expert segmentations are subject to intra- and inter-operator variability. Deep learning methods are increasingly employed to automatically carry out image segmentation tasks. These networks usually need to be trained on a large image dataset along with the manual segmentations to maximize generalization to new images, but it is not always possible to have access to a multitude of CT-scans with the associated ground truth. It then becomes necessary to use training techniques to make the best use of the limited available data. In this paper, we propose a dedicated pipeline of preprocessing, deep learning based segmentation method and post-processing for in-vivo human femurs and vertebrae segmentation from CT-scans volumes. We experimented with three U-Net architectures and showed that out-of-the-box models enable automatic and high-quality volume segmentation if carefully trained. We compared the failure load simulation results obtained on femurs and vertebrae using either automatic or manual segmentations and studied the sensitivity of the simulations on small variations of the automatic segmentation. The failure loads obtained using automatic segmentations were comparable to those obtained using manual expert segmentations for all the femurs and vertebrae tested, demonstrating the effectiveness of the automated segmentation approach for failure load simulations.

Developing and Validating a Model of Humeral Stem Primary Stability, Intended for In Silico Clinical Trials

In silico clinical trials (ISCT) can contribute to demonstrating a device's performance via credible computational models applied on virtual cohorts. Our purpose was to establish the credibility of a model for assessing the risk of humeral stem loosening in total shoulder arthroplasty, based on a twofold validation scheme involving both benchtop and clinical validation activities, for ISCT applications. A finite element model computing bone-implant micromotion (benchtop model) was quantitatively compared to a bone foam micromotion test (benchtop comparator) to ensure that the physics of the system was captured correctly. The model was expanded to a population-based approach (clinical model) and qualitatively evaluated based on its ability to replicate findings from a published clinical study (clinical comparator), namely that grit-blasted stems are at a significantly higher risk of loosening than porous-coated stems, to ensure that clinical performance of the stem can be predicted appropriately. Model form sensitivities pertaining to surgical variation and implant design were evaluated. The model replicated benchtop micromotion measurements (52.1 ± 4.3 µm), without a significant impact of the press-fit ("Press-fit": 54.0 ± 8.5 µm, "No press-fit": 56.0 ± 12.0 µm). Applied to a virtual population, the grit-blasted stems (227 ± 78µm) experienced significantly larger micromotions than porous-coated stems (162 ± 69µm), in accordance with the findings of the clinical comparator. This work provides a concrete example for evaluating the credibility of an ISCT study. By validating the modeling approach against both benchtop and clinical data, model credibility is established for an ISCT application aiming to enrich clinical data in a regulatory submission.

The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis

Bone ranks as the third most frequent tissue affected by cancer metastases, following the lung and liver. Bone metastases are often painful and may result in pathological fracture, which is a major cause of morbidity and mortality in cancer patients. To quantify fracture risk, finite element (FE) analysis has shown to be a promising tool, but metastatic lesions are typically not specifically segmented and therefore their mechanical properties may not be represented adequately. Deep learning methods potentially provide the opportunity to automatically segment these lesions and change the mechanical properties more adequately. In this study, our primary focus was to gain insight into the performance of an automatic segmentation algorithm for femoral metastatic lesions using deep learning methods and the subsequent effects on FE outcomes. The aims were to determine the similarity between manual segmentation and automatic segmentation; the differences in predicted failure load between FE models with automatically segmented osteolytic and mixed lesions and the models with CT-based lesion values (the gold standard); and the effect on the BOne Strength (BOS) score (failure load adjusted for body weight) and subsequent fracture risk assessments. From two patient cohorts, a total number of 50 femurs with osteolytic and mixed metastatic lesions were included in this study. The femurs were segmented from CT images and transferred into FE meshes. The material behavior was implemented as non-linear isotropic. These FE models were considered as gold standard (Finite Element no Segmented Lesion: FE-no-SL), whereby the local calcium equivalent density of both femur and metastatic lesion was extracted from CT-values. Lesions in the femur were manually segmented by two biomechanical experts after which final lesion segmentation for each femur was obtained based on consensus of opinions between two observers. Subsequently, a self-configuring variant of the popular deep learning model U-Net known as nnU-Net was used to automatically segment metastatic lesions within the femur. For these models with segmented lesions (Finite Element with Segmented Lesion: FE-with-SL), the calcium equivalent density within the metastatic lesions was set to zero after being segmented by the neural network, simulating absence of load-bearing capacity of these lesions. The models (either with or without automatically segmented lesions) were loaded incrementally in axial direction until failure was simulated. Dice coefficient was used to evaluate the similarity of the manual and automatic segmentation. Mean calcium equivalent density values within the automatically segmented lesions were calculated. Failure loads and patterns were determined. Furthermore, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for both groups by comparing the predictions to the occurrence or absence of actual fracture within the patient cohorts. The automatic segmentation algorithm performed in a none-robust manner. Dice coefficients describing the similarity between consented manual and automatic segmentations were relatively low (mean 0.45 ± standard deviation 0.33, median 0.54). Failure load difference between the FE-no-SL and FE-with-SL groups varied from 0 % to 48 % (mean 6.6 %). Correlation analysis of failure loads between the two groups showed a strong relationship (R2 > 0.9). From the 50 cases, four cases showed clear deviations for which models with automatic lesion segmentation (FE-with-SL) showed considerably lower failure loads. In the whole database including osteolytic and mixed lesions, sensitivity and NPV remained the same, but specificity and PPV decreased from 94 % to 83 %, and from 78 % to 54 % respectively from FE-no-SL to FE-with-SL. This study indicates that the nnU-Net yielded none-robust outcomes in femoral lesion segmentation and that other segmentation algorithms should be considered. However, the difference in failure pattern and failure load between FE models with automatically segmented osteolytic and mixed lesions were relatively small in most cases with a few exceptions. On the other hand, the accuracy of fracture risk assessment using the BOS score was lower compared to the FE-no-SL. In conclusion, this study showed that automatic lesion segmentation is a none-solved issue and therefore, quantifying lesion characteristics and the subsequent effect on the fracture risk using deep learning will remain challenging.

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

Background

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

Methods

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

Results

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

Conclusions

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

The role of bone metastases on the mechanical competence of human vertebrae

Spine is the most common site for bone metastases. The evaluation of the mechanical competence and failure location in metastatic vertebrae is a biomechanical and clinical challenge. Little is known about the failure behaviour of vertebrae with metastatic lesions. The aim of this study was to use combined micro-Computed Tomography (microCT) and time-lapsed mechanical testing to reveal the failure location in metastatic vertebrae. Fifteen spine segments, each including a metastatic and a radiologically healthy vertebra, were tested in compression up to failure within a microCT. Volumetric strains were measured using Digital Volume Correlation. The images of undeformed and deformed specimens were overlapped to identify the failure location. Vertebrae with lytic metastases experienced the largest average compressive strains (median ± standard deviation: -8506 ± 4748microstrain), followed by the vertebrae with mixed metastases (-7035 ± 15605microstrain), the radiologically healthy vertebrae (-5743 ± 5697microstrain), and the vertebrae with blastic metastases (-3150 ± 4641microstrain). Strain peaks were localised within and nearby the lytic lesions or around the blastic tissue. Failure between the endplate and the metastasis was identified in vertebrae with lytic metastases, whereas failure localised around the metastasis in vertebrae with blastic lesions. This study showed for the first time the role of metastases on the vertebral internal deformations. While lytic lesions lead to failure of the metastatic vertebra, vertebrae with blastic metastases are more likely to induce failure in the adjacent vertebrae. Nevertheless, every metastatic lesion affects the vertebral deformation differently, making it essential to assess how the lesion affects the bone microstructure. These results suggest that the properties of the lesion (type, size, location within the vertebral body) should be considered when developing clinical tools to predict the risk of fracture in patients with metastatic lesions.

Evaluation of Load-To-Strength Ratios in Metastatic Vertebrae and Comparison With Age- and Sex-Matched Healthy Individuals

Vertebrae containing osteolytic and osteosclerotic bone metastases undergo pathologic vertebral fracture (PVF) when the lesioned vertebrae fail to carry daily loads. We hypothesize that task-specific spinal loading patterns amplify the risk of PVF, with a higher degree of risk in osteolytic than in osteosclerotic vertebrae. To test this hypothesis, we obtained clinical CT images of 11 cadaveric spines with bone metastases, estimated the individual vertebral strength from the CT data, and created spine-specific musculoskeletal models from the CT data. We established a musculoskeletal model for each spine to compute vertebral loading for natural standing, natural standing + weights, forward flexion + weights, and lateral bending + weights and derived the individual vertebral load-to-strength ratio (LSR). For each activity, we compared the metastatic spines' predicted LSRs with the normative LSRs generated from a population-based sample of 250 men and women of comparable ages. Bone metastases classification significantly affected the CT-estimated vertebral strength (Kruskal-Wallis, p < 0.0001). Post-test analysis showed that the estimated vertebral strength of osteosclerotic and mixed metastases vertebrae was significantly higher than that of osteolytic vertebrae (p = 0.0016 and p = 0.0003) or vertebrae without radiographic evidence of bone metastasis (p = 0.0010 and p = 0.0003). Compared with the median (50%) LSRs of the normative dataset, osteolytic vertebrae had higher median (50%) LSRs under natural standing (p = 0.0375), natural standing + weights (p = 0.0118), and lateral bending + weights (p = 0.0111). Surprisingly, vertebrae showing minimal radiographic evidence of bone metastasis presented significantly higher median (50%) LSRs under natural standing (p < 0.0001) and lateral bending + weights (p = 0.0009) than the normative dataset. Osteosclerotic vertebrae had lower median (50%) LSRs under natural standing (p < 0.0001), natural standing + weights (p = 0.0005), forward flexion + weights (p < 0.0001), and lateral bending + weights (p = 0.0002), a trend shared by vertebrae with mixed lesions. This study is the first to apply musculoskeletal modeling to estimate individual vertebral loading in pathologic spines and highlights the role of task-specific loading in augmenting PVF risk associated with specific bone metastatic types. Our finding of high LSRs in vertebrae without radiologically observed bone metastasis highlights that patients with metastatic spine disease could be at an increased risk of vertebral fractures even at levels where lesions have not been identified radiologically.

Toward an artificial intelligence-assisted framework for reconstructing the digital twin of vertebra and predicting its fracture response

This article presents an effort toward building an artificial intelligence (AI) assisted framework, coined ReconGAN, for creating a realistic digital twin of the human vertebra and predicting the risk of vertebral fracture (VF). ReconGAN consists of a deep convolutional generative adversarial network (DCGAN), image-processing steps, and finite element (FE) based shape optimization to reconstruct the vertebra model. This DCGAN model is trained using a set of quantitative micro-computed tomography (micro-QCT) images of the trabecular bone obtained from cadaveric samples. The quality of synthetic trabecular models generated using DCGAN are verified by comparing a set of its statistical microstructural descriptors with those of the imaging data. The synthesized trabecular microstructure is then infused into the vertebra cortical shell extracted from the patient's diagnostic CT scans using an FE-based shape optimization approach to achieve a smooth transition between trabecular to cortical regions. The final geometrical model of the vertebra is converted into a high-fidelity FE model to simulate the VF response using a continuum damage model under compression and flexion loading conditions. A feasibility study is presented to demonstrate the applicability of digital twins generated using this AI-assisted framework to predict the risk of VF in a cancer patient with spinal metastasis.

Osteolytic vs. Osteoblastic Metastatic Lesion: Computational Modeling of the Mechanical Behavior in the Human Vertebra after Screws Fixation Procedure

Metastatic lesions compromise the mechanical integrity of vertebrae, increasing the fracture risk. Screw fixation is usually performed to guarantee spinal stability and prevent dramatic fracture events. Accordingly, predicting the overall mechanical response in such conditions is critical to planning and optimizing surgical treatment. This work proposes an image-based finite element computational approach describing the mechanical behavior of a patient-specific instrumented metastatic vertebra by assessing the effect of lesion size, location, type, and shape on the fracture load and fracture patterns under physiological loading conditions. A specific constitutive model for metastasis is integrated to account for the effect of the diseased tissue on the bone material properties. Computational results demonstrate that size, location, and type of metastasis significantly affect the overall vertebral mechanical response and suggest a better way to account for these parameters in estimating the fracture risk. Combining multiple osteolytic lesions to account for the irregular shape of the overall metastatic tissue does not significantly affect the vertebra fracture load. In addition, the combination of loading mode and metastasis type is shown for the first time as a critical modeling parameter in determining fracture risk. The proposed computational approach moves toward defining a clinically integrated tool to improve the management of metastatic vertebrae and quantitatively evaluate fracture risk.

Influence of Metastatic Bone Lesion Type and Tumor Origin on Human Vertebral Bone Architecture, Matrix Quality, and Mechanical Properties

Metastatic spine disease is incurable, causing increased vertebral fracture risk and severe patient morbidity. Here, we demonstrate that osteolytic, osteosclerotic, and mixed bone metastasis uniquely modify human vertebral bone architecture and quality, affecting vertebral strength and stiffness. Multivariable analysis showed bone metastasis type dominates vertebral strength and stiffness changes, with neither age nor gender having an independent effect. In osteolytic vertebrae, bone architecture rarefaction, lower tissue mineral content and connectivity, and accumulation of advanced glycation end-products (AGEs) affected low vertebral strength and stiffness. In osteosclerotic vertebrae, high trabecular number and thickness but low AGEs, suggesting a high degree of bone remodeling, yielded high vertebral strength. Our study found that bone metastasis from prostate and breast primary cancers differentially impacted the osteosclerotic bone microenvironment, yielding altered bone architecture and accumulation of AGEs. These findings indicate that therapeutic approaches should target the restoration of bone structural integrity. © 2022 American Society for Bone and Mineral Research (ASBMR).

Fabric-elasticity relationships of tibial trabecular bone are similar in osteogenesis imperfecta and healthy individuals

Osteogenesis Imperfecta (OI) is an inherited form of bone fragility characterised by impaired synthesis of type I collagen, altered trabecular bone architecture and reduced bone mass. High resolution peripheral computed tomography (HR-pQCT) is a powerful method to investigate bone morphology at peripheral sites including the weight-bearing distal tibia. The resulting 3D reconstructions can be used as a basis of micro-finite element (FE) or homogenized finite element (hFE) models for bone strength estimation. The hFE scheme uses homogenized local bone volume fraction (BV/TV) and anisotropy information (fabric) to compute healthy bone strength within a reasonable computation time using fabric-elasticity relationships. However, it is unclear if these relationships quantified previously for healthy controls are valid for trabecular bone from OI patients. Thus, the aim of this study is to investigate fabric-elasticity relationships in OI trabecular bone compared to healthy controls. In the present study, the morphology of distal tibiae from 50 adults with OI were compared to 120 healthy controls using second generation HR-pQCT. Six cubic regions of interest (ROIs) were selected per individual in a common anatomical region. A first matching between OI and healthy control group was performed by selecting similar individuals to obtain identical mean and median age and sex distribution. It allowed us to perform a first morphometric analysis and compare the outcome with literature. Then, stiffness tensors of the ROIs were computed using μFE and multiple linear regressions were performed with the Zysset-Curnier orthotropic fabric-elasticity model. An initial fit was performed on both the OI group and the healthy control group using all extracted ROIs. Then, data was filtered according to a fixed threshold for a defined coefficient of variation (CV) assessing ROI heterogeneity and additional linear regressions were performed on these filtered data sets. These full and filtered data were in turn compared with previous results from μCT reconstructions obtained in other anatomical locations. Finally, the ROIs of both groups were matched according to their BV/TV and degree of anisotropy (DA). Linear regressions were performed using these matched data to detect statistical differences between the two groups. Compared to healthy controls, we found the OI samples to have significantly lower BV/TV and trabecular number (Tb.N.), significantly higher CV, trabecular separation (Tb.Sp.) and trabecular separation standard deviation (Tb.Sp.SD), but no differences in trabecular thickness (Tb.Th.). These results are in agreement with previous studies. The stiffnesses of highly heterogeneous ROIs were randomly lower with respect to the fabric-elasticity relationships, which reflects the limit of validity of the computational homogenisation methodology. This limitation does not challenge the fabric-elasticity relationship, which extrapolation to heterogeneous ROIs is probably reasonable but can simply not be evaluated with the employed homogenisation methodology. Moreover, due to their low BV/TV, the potential (unknown) errors on these heterogeneous ROIs would have negligible influence on whole bone stiffness in comparison to homogeneous ROIs which are orders of magnitude stiffer. The filtering of highly heterogeneous ROIs removed these low stiffness ROIs and led to similar correlation coefficients for both OI and healthy groups. Finally, the BV/TV and DA matched data revealed no significant differences in fabric-elasticity parameters between OI and healthy individuals. Moreover, the filtering step did not exclude a particular OI type. Compared to previous studies, the stiffness constants from the 61 μm resolution HR-pQCT ROIs were lower than for the 36 μm resolution μCT ROIs. In conclusion, OI trabecular bone of the distal tibia was shown to be significantly more heterogeneous and have a lower BV/TV than healthy controls. Despite the reduced linear regression parameters found for HR-pQCT images, the fabric-elasticity relationships between OI and healthy individuals are similar when the trabecular bone ROIs are sufficiently homogeneous to perform the computational stiffness analysis. Accordingly, the elastic properties used for FEA of healthy bones are also valid for OI bones.

MicroFE models of porcine vertebrae with induced bone focal lesions: Validation of predicted displacements with digital volume correlation

The evaluation of the local mechanical behavior as a result of metastatic lesions is fundamental for the characterization of the mechanical competence of metastatic vertebrae. Micro finite element (microFE) models have the potential of addressing this challenge through laboratory studies but their predictions of local deformation due to the complexity of the bone structure compromized by the lesion must be validated against experiments. In this study, the displacements predicted by homogeneous, linear and isotropic microFE models of vertebrae were validated against experimental Digital Volume Correlation (DVC) measurements. Porcine spine segments, with and without mechanically induced focal lesions, were tested in compression within a micro computed tomography (microCT) scanner. The displacement within the bone were measured with an optimized global DVC approach (BoneDVC). MicroFE models of the intact and lesioned vertebrae, including or excluding the growth plates, were developed from the microCT images. The microFE and DVC boundary conditions were matched. The displacements measured by the DVC and predicted by the microFE along each Cartesian direction were compared. The results showed an excellent agreement between the measured and predicted displacements, both for intact and metastatic vertebrae, in the middle of the vertebra, in those cases where the structure was not loaded beyond yield (0.69 < R² < 1.00). Models with growth plates showed the worst correlations (0.02 < R² < 0.99), while a clear improvement was observed if the growth plates were excluded (0.56 < R² < 1.00). In conclusion, these simplified models can predict complex displacement fields in the elastic regime with high reliability, more complex non-linear models should be implemented to predict regions with high deformation, when the bone is loaded beyond yield.

Finite Element Analysis of Osteoporotic and Osteoblastic Vertebrae and Its Association With the Proton Density Fat Fraction From Chemical Shift Encoding-Based Water-Fat MRI - A Preliminary Study

PURPOSE

Osteoporosis is prevalent and entails alterations of vertebral bone and marrow. Yet, the spine is also a common site of metastatic spread. Parameters that can be non-invasively measured and could capture these alterations are the volumetric bone mineral density (vBMD), proton density fat fraction (PDFF) as an estimate of relative fat content, and failure displacement and load from finite element analysis (FEA) for assessment of bone strength. This study's purpose was to investigate if osteoporotic and osteoblastic metastatic changes in lumbar vertebrae can be differentiated based on the abovementioned parameters (vBMD, PDFF, and measures from FEA), and how these parameters correlate with each other.

MATERIALS AND METHODS

Seven patients (3 females, median age: 77.5 years) who received 3-Tesla magnetic resonance imaging (MRI) and multi-detector computed tomography (CT) of the lumbar spine and were diagnosed with either osteoporosis (4 patients) or diffuse osteoblastic metastases (3 patients) were included. Chemical shift encoding-based water-fat MRI (CSE-MRI) was used to extract the PDFF, while vBMD was extracted after automated vertebral body segmentation using CT. Segmentation masks were used for FEA-based failure displacement and failure load calculations. Failure displacement, failure load, and PDFF were compared between patients with osteoporotic vertebrae versus patients with osteoblastic metastases, considering non-fractured vertebrae (L1-L4). Associations between those parameters were assessed using Spearman correlation.

RESULTS

Median vBMD was 59.3 mg/cm³ in osteoporotic patients. Median PDFF was lower in the metastatic compared to the osteoporotic patients (11.9% vs. 43.8%, p=0.032). Median failure displacement and failure load were significantly higher in metastatic compared to osteoporotic patients (0.874 mm vs. 0.348 mm, 29,589 N vs. 3,095 N, p=0.034 each). A strong correlation was noted between PDFF and failure displacement (rho -0.679, p=0.094). A very strong correlation was noted between PDFF and failure load (rho -0.893, p=0.007).

CONCLUSION

PDFF as well as failure displacement and load allowed to distinguish osteoporotic from diffuse osteoblastic vertebrae. Our findings further show strong associations between PDFF and failure displacement and load, thus may indicate complimentary pathophysiological associations derived from two non-invasive techniques (CSE-MRI and CT) that inherently measure different properties of vertebral bone and marrow.

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.

Fracture Risk Evaluation of Bone Metastases: A Burning Issue

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

Type, size, and position of metastatic lesions explain the deformation of the vertebrae under complex loading conditions

BACKGROUND

Bone metastases may lead to spine instability and increase the risk of fracture. Scoring systems are available to assess critical metastases, but they lack specificity, and provide uncertain indications over a wide range, where most cases fall. The aim of this work was to use a novel biomechanical approach to evaluate the effect of lesion type, size, and location on the deformation of the metastatic vertebra.

METHOD

Vertebrae with metastases were identified from 16 human spines from a donation programme. The size and position of the metastases, and the Spine Instability Neoplastic Score (SINS) were evaluated from clinical Quantitative Computed Tomography images. Thirty-five spine segments consisting of metastatic vertebrae and adjacent healthy controls were biomechanically tested in four different loading conditions. The strain distribution over the entire vertebral bodies was measured with Digital Image Correlation. Correlations between the features of the metastasis (type, size, position and SINS) and the deformation of the metastatic vertebrae were statistically explored.

RESULTS

The metastatic type (lytic, blastic, mixed) characterizes the vertebral behaviour (Kruskal-Wallis, p = 0.04). In fact, the lytic metastases showed more critical deformation compared to the control vertebrae (average: 2-fold increase, with peaks of 14-fold increase). By contrast, the vertebrae with mixed or blastic metastases did not show a clear trend, with deformations similar or lower than the controls. Once the position of the lytic lesion with respect to the loading direction was taken into account, the size of the lesion was significantly correlated with the perturbation to the strain distribution (r² = 0.72, p < 0.001). Conversely, the SINS poorly correlated with the mechanical evidence, and only in case of lytic lesions (r² = 0.25, p < 0.0001).

CONCLUSION

These results highlight the relevance of the size and location of the lytic lesion, which are marginally considered in the current clinical scoring systems, in driving the spinal biomechanical instability. The strong correlation with the biomechanical evidence indicates that these parameters are representative of the mechanical competence of the vertebra. The improved explanatory power compared to the SINS suggests including them in future guidelines for the clinical practice.

Improved estimates of strength and stiffness in pathologic vertebrae with bone metastases using CT-derived bone density compared with radiographic bone lesion quality classification

OBJECTIVE

The aim of this study was to compare the ability of 1) CT-derived bone lesion quality (classification of vertebral bone metastases [BM]) and 2) computed CT-measured volumetric bone mineral density (vBMD) for evaluating the strength and stiffness of cadaver vertebrae from donors with metastatic spinal disease.

METHODS

Forty-five thoracic and lumbar vertebrae were obtained from cadaver spines of 11 donors with breast, esophageal, kidney, lung, or prostate cancer. Each vertebra was imaged using microCT (21.4 μm), vBMD, and bone volume to total volume were computed, and compressive strength and stiffness experimentally measured. The microCT images were reconstructed at 1-mm voxel size to simulate axial and sagittal clinical CT images. Five expert clinicians blindly classified the images according to bone lesion quality (osteolytic, osteoblastic, mixed, or healthy). Fleiss' kappa test was used to test agreement among 5 clinical raters for classifying bone lesion quality. Kruskal-Wallis ANOVA was used to test the difference in vertebral strength and stiffness based on bone lesion quality. Multivariable regression analysis was used to test the independent contribution of bone lesion quality, computed vBMD, age, gender, and race for predicting vertebral strength and stiffness.

RESULTS

A low interrater agreement was found for bone lesion quality (κ = 0.19). Although the osteoblastic vertebrae showed significantly higher strength than osteolytic vertebrae (p = 0.0148), the multivariable analysis showed that bone lesion quality explained 19% of the variability in vertebral strength and 13% in vertebral stiffness. The computed vBMD explained 75% of vertebral strength (p < 0.0001) and 48% of stiffness (p < 0.0001) variability. The type of BM affected vBMD-based estimates of vertebral strength, explaining 75% of strength variability in osteoblastic vertebrae (R2 = 0.75, p < 0.0001) but only 41% in vertebrae with mixed bone metastasis (R2 = 0.41, p = 0.0168), and 39% in osteolytic vertebrae (R2 = 0.39, p = 0.0381). For vertebral stiffness, vBMD was only associated with that of osteoblastic vertebrae (R2 = 0.44, p = 0.0024). Age and race inconsistently affected the model's strength and stiffness predictions.

CONCLUSIONS

Pathologic vertebral fracture occurs when the metastatic lesion degrades vertebral strength, rendering it unable to carry daily loads. This study demonstrated the limitation of qualitative clinical classification of bone lesion quality for predicting pathologic vertebral strength and stiffness. Computed CT-derived vBMD more reliably estimated vertebral strength and stiffness. Replacing the qualitative clinical classification with computed vBMD estimates may improve the prediction of vertebral fracture risk.

Large Lytic Defects Produce Kinematic Instability and Loss of Compressive Strength in Human Spines: An in Vitro Study

BACKGROUND

In patients with spinal metastases, kinematic instability is postulated to be a predictor of pathologic vertebral fractures. However, the relationship between this kinematic instability and the loss of spinal strength remains unknown.

METHODS

Twenty-four 3-level thoracic and lumbar segments from 8 cadaver spines from female donors aged 47 to 69 years were kinematically assessed in axial compression (180 N) and axial compression with a flexion or extension moment (7.5 Nm). Two patterns of lytic defects were mechanically simulated: (1) a vertebral body defect, corresponding to Taneichi model C (n = 13); and (2) the model-C defect plus destruction of the ipsilateral pedicle and facet joint, corresponding to Taneichi model E (n = 11). The kinematic response was retested, and compression strength was measured. Two-way repeated-measures analysis of variance was used to test the effect of each model on the kinematic response of the segment. Multivariable linear regression was used to test the association between the kinematic parameters and compressive strength of the segment.

RESULTS

Under a flexion moment, and for both models C and E, the lesioned spines exhibited greater flexion range of motion (ROM) and axial translation than the control spines. Both models C and E caused lower extension ROM and greater axial, sagittal, and transverse translation under an extension moment compared with the control spines. Two-way repeated-measures analysis revealed that model E, compared with model C, caused significantly greater changes in extension and torsional ROM under an extension moment, and greater sagittal translation under a flexion moment. For both models C and E, greater differences in flexion ROM and sagittal translation under a flexion moment, and greater differences in extension ROM and in axial and transverse translation under an extension moment, were associated with lower compressive strength of the lesioned spines.

CONCLUSIONS

Critical spinal lytic defects result in kinematic abnormalities and lower the compressive strength of the spine.

CLINICAL RELEVANCE

This experimental study demonstrates that lytic foci degrade the kinematic stability and compressive strength of the spine. Understanding the mechanisms for this degradation will help to guide treatment decisions that address inferred instability and fracture risk in patients with metastatic spinal disease.