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Amini M, Reisinger A, Synek A, Hirtler L, Pahr D. The predictive ability of a QCT-FE model of the proximal femoral stiffness under multiple load cases is strongly influenced by experimental uncertainties. J Mech Behav Biomed Mater 2023; 139:105664. [PMID: 36657193 DOI: 10.1016/j.jmbbm.2023.105664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 01/03/2023] [Accepted: 01/05/2023] [Indexed: 01/11/2023]
Abstract
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.
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Affiliation(s)
- Morteza Amini
- Institute of Lightweight Design and Structural Biomechanics, TU Wien, Getreidemarkt 9, 1060, Vienna, Austria.
| | - Andreas Reisinger
- Division Biomechanics, Karl Landsteiner University of Health Sciences, Dr.-Karl-Dorrek-Straße 30, 3500 Krems an der Donau, Austria.
| | - Alexander Synek
- Institute of Lightweight Design and Structural Biomechanics, TU Wien, Getreidemarkt 9, 1060, Vienna, Austria.
| | - Lena Hirtler
- Center for Anatomy and Cell Biology, Medical University of Vienna, Währinger Straße 13, 1090, Vienna, Austria.
| | - Dieter Pahr
- Institute of Lightweight Design and Structural Biomechanics, TU Wien, Getreidemarkt 9, 1060, Vienna, Austria; Division Biomechanics, Karl Landsteiner University of Health Sciences, Dr.-Karl-Dorrek-Straße 30, 3500 Krems an der Donau, Austria.
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Dudle A, Gugler Y, Pretterklieber M, Ferrari S, Lippuner K, Zysset P. 2D-3D reconstruction of the proximal femur from DXA scans: Evaluation of the 3D-Shaper software. Front Bioeng Biotechnol 2023; 11:1111020. [PMID: 36937766 PMCID: PMC10014626 DOI: 10.3389/fbioe.2023.1111020] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 02/15/2023] [Indexed: 03/05/2023] Open
Abstract
Introduction: Osteoporosis is currently diagnosed based on areal bone mineral density (aBMD) computed from 2D DXA scans. However, aBMD is a limited surrogate for femoral strength since it does not account for 3D bone geometry and density distribution. QCT scans combined with finite element (FE) analysis can deliver improved femoral strength predictions. However, non-negligible radiation dose and high costs prevent a systematic usage of this technique for screening purposes. As an alternative, the 3D-Shaper software (3D-Shaper Medical, Spain) reconstructs the 3D shape and density distribution of the femur from 2D DXA scans. This approach could deliver a more accurate estimation of femoral strength than aBMD by using FE analysis on the reconstructed 3D DXA. Methods: Here we present the first independent evaluation of the software, using a dataset of 77 ex vivo femora. We extend a prior evaluation by including the density distribution differences, the spatial correlation of density values and an FE analysis. Yet, cortical thickness is left out of this evaluation, since the cortex is not resolved in our FE models. Results: We found an average surface distance of 1.16 mm between 3D DXA and QCT images, which shows a good reconstruction of the bone geometry. Although BMD values obtained from 3D DXA and QCT correlated well (r 2 = 0.92), the 3D DXA BMD were systematically lower. The average BMD difference amounted to 64 mg/cm3, more than one-third of the 3D DXA BMD. Furthermore, the low correlation (r 2 = 0.48) between density values of both images indicates a limited reconstruction of the 3D density distribution. FE results were in good agreement between QCT and 3D DXA images, with a high coefficient of determination (r 2 = 0.88). However, this correlation was not statistically different from a direct prediction by aBMD. Moreover, we found differences in the fracture patterns between the two image types. QCT-based FE analysis resulted mostly in femoral neck fractures and 3D DXA-based FE in subcapital or pertrochanteric fractures. Discussion: In conclusion, 3D-Shaper generates an altered BMD distribution compared to QCT but, after careful density calibration, shows an interesting potential for deriving a standardized femoral strength from a DXA scan.
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Affiliation(s)
- Alice Dudle
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- *Correspondence: Alice Dudle, ; Yvan Gugler,
| | - Yvan Gugler
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- *Correspondence: Alice Dudle, ; Yvan Gugler,
| | - Michael Pretterklieber
- Division of Anatomy, Gottfried Schatz Research Center, Medical University of Graz, Graz, Austria
- Division of Anatomy, Center for Anatomy and Cell Biology, Medical University of Vienna, Vienna, Austria
| | - Serge Ferrari
- Division of Bone Diseases, Geneva University Hospitals (HUG), Geneva, Switzerland
| | - Kurt Lippuner
- Department of Osteoporosis, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Philippe Zysset
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
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Confavreux CB, Follet H, Mitton D, Pialat JB, Clézardin P. Fracture Risk Evaluation of Bone Metastases: A Burning Issue. Cancers (Basel) 2021; 13:cancers13225711. [PMID: 34830865 PMCID: PMC8616502 DOI: 10.3390/cancers13225711] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 11/07/2021] [Accepted: 11/10/2021] [Indexed: 11/16/2022] Open
Abstract
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.
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Affiliation(s)
- Cyrille B. Confavreux
- Centre Expert des Métastases Osseuses (CEMOS), Département de Rhumatologie, Institut de Cancérologie des Hospices Civils de Lyon (IC-HCL), Hôpital Lyon Sud, Hospices Civils de Lyon, 69310 Pierre Bénite, France
- Université de Lyon, Université Claude Bernard Lyon 1, 69100 Villeurbanne, France; (H.F.); (J.B.P.); (P.C.)
- Institut National de la Santé et de la Recherche Médicale INSERM, LYOS UMR1033, 69008 Lyon, France
- Correspondence:
| | - Helene Follet
- Université de Lyon, Université Claude Bernard Lyon 1, 69100 Villeurbanne, France; (H.F.); (J.B.P.); (P.C.)
- Institut National de la Santé et de la Recherche Médicale INSERM, LYOS UMR1033, 69008 Lyon, France
| | - David Mitton
- Université de Lyon, Université Gustave Eiffel, Université Claude Bernard Lyon 1, LBMC, UMR_T 9406, 69622 Lyon, France;
| | - Jean Baptiste Pialat
- Université de Lyon, Université Claude Bernard Lyon 1, 69100 Villeurbanne, France; (H.F.); (J.B.P.); (P.C.)
- CREATIS, CNRS UMR 5220, INSERM U1294, INSA Lyon, Université Jean Monnet Saint-Etienne, 42000 Saint-Etienne, France
- Service de Radiologie, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, 69310 Pierre Bénite, France
| | - Philippe Clézardin
- Université de Lyon, Université Claude Bernard Lyon 1, 69100 Villeurbanne, France; (H.F.); (J.B.P.); (P.C.)
- Institut National de la Santé et de la Recherche Médicale INSERM, LYOS UMR1033, 69008 Lyon, France
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Fracture Risk of Long Bone Metastases: A Review of Current and New Decision-Making Tools for Prophylactic Surgery. Cancers (Basel) 2021; 13:cancers13153662. [PMID: 34359563 PMCID: PMC8345078 DOI: 10.3390/cancers13153662] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 07/16/2021] [Accepted: 07/18/2021] [Indexed: 02/06/2023] Open
Abstract
Simple Summary Long bone metastases are frequently a pivotal point in the oncological history of patients. Weakening of the bone results in pathologic fractures that not only compromise patient function but also their survival. Therefore, the main issue for tumor boards remains timely assessment of the risk of fracture, as this is a key consideration in providing preventive surgery while also avoiding overtreatment. As the Mirels scoring system takes into account both the radiological and the clinical criteria, it has been used worldwide since the 1990s. However, due to increasing concern regarding the lack of accuracy, new thresholds have been defined for the identification of impending fractures that require prophylactic surgery, on the basis of axial cortical involvement and biomechanical models involving quantitative computed tomography. The aim of this review is to establish a state-of-the-art of the risk assessment of long bone metastases fractures, from simple radiologic scores to more complex multidimensional bone models, in order to define new decision-making tools. Abstract Long bone pathological fractures very much reflect bone metastases morbidity in many types of cancer. Bearing in mind that they not only compromise patient function but also survival, identifying impending fractures before the actual event is one of the main concerns for tumor boards. Indeed, timely prophylactic surgery has been demonstrated to increase patient quality of life as well as survival. However, early surgery for long bone metastases remains controversial as the current fracture risk assessment tools lack accuracy. This review first focuses on the gold standard Mirels rating system. It then explores other unique imaging thresholds such as axial or circumferential cortical involvement and the merits of nuclear imaging tools. To overcome the lack of specificity, other fracture prediction strategies have focused on biomechanical models based on quantitative computed tomography (CT): computed tomography rigidity analysis (CT-RA) and finite element analysis (CT-FEA). Despite their higher specificities in impending fracture assessment, their limited availability, along with a need for standardization, have limited their use in everyday practice. Currently, the prediction of long bone pathologic fractures is a multifactorial process. In this regard, machine learning could potentially be of value by taking into account clinical survival prediction as well as clinical and improved CT-RA/FEA data.
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Femoral strength can be predicted from 2D projections using a 3D statistical deformation and texture model with finite element analysis. Med Eng Phys 2021; 93:72-82. [PMID: 34154777 DOI: 10.1016/j.medengphy.2021.05.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 04/11/2021] [Accepted: 05/18/2021] [Indexed: 11/22/2022]
Abstract
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.
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Ghouchani A, Rouhi G, Ebrahimzadeh MH. Post-operative fracture risk assessment following tumor curettage in the distal femur: a hybrid in vitro and in silico biomechanical approach. Sci Rep 2020; 10:21319. [PMID: 33288803 PMCID: PMC7721712 DOI: 10.1038/s41598-020-78188-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 11/17/2020] [Indexed: 12/26/2022] Open
Abstract
The distal femur is the predominant site for benign bone tumours and a common site for fracture following tumour removal or cementation. However, the lack of conclusive assessment criterion for post-operative fracture risk and appropriate devices for cement augmentation are serious concerns. Hence, a validated biomechanical tool was developed to assess bone strength, depending on the size and location of artificially created tumorous defects in the distal femora. The mechanics of the bone–cement interface was investigated to determine the main causes of reconstruction failure. Based on quantitative-CT images, non-linear and heterogeneous finite element (FE) models of human cadaveric distal femora with simulated tumourous defects were created and validated using in vitro mechanical tests from 14 cadaveric samples. Statistical analyses demonstrated a strong linear relationship (R2 = 0.95, slope = 1.12) with no significant difference between bone strengths predicted by in silico analyses and in vitro tests (P = 0.174). FE analyses showed little reduction in bone strength until the defect was 35% or more of epiphyseal volume, and reduction in bone strength was less pronounced for laterally located defects than medial side defects. Moreover, the proximal end of the cortical window and the most interior wall of the bone–cement interface were the most vulnerable sites for reconstruction failure.
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Affiliation(s)
- Azadeh Ghouchani
- Faculty of Biomedical Engineering, Amirkabir University of Technology, No. 350, Hafez Ave, Valiasr Square, 1591634311, Tehran, Iran
| | - Gholamreza Rouhi
- Faculty of Biomedical Engineering, Amirkabir University of Technology, No. 350, Hafez Ave, Valiasr Square, 1591634311, Tehran, Iran.
| | - Mohammad Hosein Ebrahimzadeh
- Orthopaedic Research Center, Department of Orthopaedic Surgery, Mashhad University of Medical Sciences, Ghaem Hospital, Ahmad Abad Street, Mashhad, Iran
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Falcinelli C, Whyne C. Image-based finite-element modeling of the human femur. Comput Methods Biomech Biomed Engin 2020; 23:1138-1161. [PMID: 32657148 DOI: 10.1080/10255842.2020.1789863] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Fracture is considered a critical clinical endpoint in skeletal pathologies including osteoporosis and bone metastases. However, current clinical guidelines are limited with respect to identifying cases at high risk of fracture, as they do not account for many mechanical determinants that contribute to bone fracture. Improving fracture risk assessment is an important area of research with clear clinical relevance. Patient-specific numerical musculoskeletal models generated from diagnostic images are widely used in biomechanics research and may provide the foundation for clinical tools used to quantify fracture risk. However, prior to clinical translation, in vitro validation of predictions generated from such numerical models is necessary. Despite adopting radically different models, in vitro validation of image-based finite element (FE) models of the proximal femur (predicting strains and failure loads) have shown very similar, encouraging levels of accuracy. The accuracy of such in vitro models has motivated their application to clinical studies of osteoporotic and metastatic fractures. Such models have demonstrated promising but heterogeneous results, which may be explained by the lack of a uniform strategy with respect to FE modeling of the human femur. This review aims to critically discuss the state of the art of image-based femoral FE modeling strategies, highlighting principal features and differences among current approaches. Quantitative results are also reported with respect to the level of accuracy achieved from in vitro evaluations and clinical applications and are used to motivate the adoption of a standardized approach/workflow for image-based FE modeling of the femur.
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Affiliation(s)
- Cristina Falcinelli
- Orthopaedic Biomechanics Laboratory, Sunnybrook Research Institute, Toronto, Canada
| | - Cari Whyne
- Orthopaedic Biomechanics Laboratory, Sunnybrook Research Institute, Toronto, Canada
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Fleps I, Bahaloo H, Zysset PK, Ferguson SJ, Pálsson H, Helgason B. Empirical relationships between bone density and ultimate strength: A literature review. J Mech Behav Biomed Mater 2020; 110:103866. [PMID: 32957183 DOI: 10.1016/j.jmbbm.2020.103866] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 05/06/2020] [Accepted: 05/17/2020] [Indexed: 02/02/2023]
Abstract
INTRODUCTION Ultimate strength-density relationships for bone have been reported with widely varying results. Reliable bone strength predictions are crucial for many applications that aim to assess bone failure. Bone density and bone morphology have been proposed to explain most of the variance in measured bone strength. If this holds true, it could lead to the derivation of a single ultimate strength-density-morphology relationship for all anatomical sites. METHODS All relevant literature was reviewed. Ultimate strength-density relationships derived from mechanical testing of human bone tissue were included. The reported relationships were translated to ultimate strength-apparent density relationships and normalized with respect to strain rate. Results were grouped based on bone tissue type (cancellous or cortical), anatomical site, and loading mode (tension vs. compression). When possible, the relationships were compared to existing ultimate strength-density-morphology relationships. RESULTS Relationships that considered bone density and morphology covered the full spectrum of eight-fold inter-study difference in reported compressive ultimate strength-density relationships for trabecular bone. This was true for studies that tested specimens in different loading direction and tissue from different anatomical sites. Sparse data was found for ultimate strength-density relationships in tension and for cortical bone properties transverse to the main loading axis of the bone. CONCLUSIONS Ultimate strength-density-morphology relationships could explain measured strength across anatomical sites and loading directions. We recommend testing of bone specimens in other directions than along the main trabecular alignment and to include bone morphology in studies that investigate bone material properties. The lack of tensile strength data did not allow for drawing conclusions on ultimate strength-density-morphology relationships. Further studies are needed. Ideally, these studies would investigate both tensile and compressive strength-density relationships, including morphology, to close this gap and lead to more accurate evaluation of bone failure.
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Affiliation(s)
- Ingmar Fleps
- Institute for Biomechanics, ETH-Zürich, Zürich, Switzerland.
| | - Hassan Bahaloo
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Philippe K Zysset
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | | | - Halldór Pálsson
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
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Mosleh H, Rouhi G, Ghouchani A, Bagheri N. Prediction of fracture risk of a distal femur reconstructed with bone cement: QCSRA, FEA, and in-vitro cadaver tests. Phys Eng Sci Med 2020. [DOI: 10.1007/s13246-020-00848-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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10
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Alcântara ACS, Assis I, Prada D, Mehle K, Schwan S, Costa-Paiva L, Skaf MS, Wrobel LC, Sollero P. Patient-Specific Bone Multiscale Modelling, Fracture Simulation and Risk Analysis-A Survey. MATERIALS (BASEL, SWITZERLAND) 2019; 13:E106. [PMID: 31878356 PMCID: PMC6981613 DOI: 10.3390/ma13010106] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 12/16/2019] [Accepted: 12/17/2019] [Indexed: 12/26/2022]
Abstract
This paper provides a starting point for researchers and practitioners from biology, medicine, physics and engineering who can benefit from an up-to-date literature survey on patient-specific bone fracture modelling, simulation and risk analysis. This survey hints at a framework for devising realistic patient-specific bone fracture simulations. This paper has 18 sections: Section 1 presents the main interested parties; Section 2 explains the organzation of the text; Section 3 motivates further work on patient-specific bone fracture simulation; Section 4 motivates this survey; Section 5 concerns the collection of bibliographical references; Section 6 motivates the physico-mathematical approach to bone fracture; Section 7 presents the modelling of bone as a continuum; Section 8 categorizes the surveyed literature into a continuum mechanics framework; Section 9 concerns the computational modelling of bone geometry; Section 10 concerns the estimation of bone mechanical properties; Section 11 concerns the selection of boundary conditions representative of bone trauma; Section 12 concerns bone fracture simulation; Section 13 presents the multiscale structure of bone; Section 14 concerns the multiscale mathematical modelling of bone; Section 15 concerns the experimental validation of bone fracture simulations; Section 16 concerns bone fracture risk assessment. Lastly, glossaries for symbols, acronyms, and physico-mathematical terms are provided.
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Affiliation(s)
- Amadeus C. S. Alcântara
- Department of Computational Mechanics, School of Mechanical Engineering, University of Campinas—UNICAMP, Campinas, Sao Paulo 13083-860, Brazil; (A.C.S.A.); (D.P.)
| | - Israel Assis
- Department of Integrated Systems, School of Mechanical Engineering, University of Campinas—UNICAMP, Campinas, Sao Paulo 13083-860, Brazil;
| | - Daniel Prada
- Department of Computational Mechanics, School of Mechanical Engineering, University of Campinas—UNICAMP, Campinas, Sao Paulo 13083-860, Brazil; (A.C.S.A.); (D.P.)
| | - Konrad Mehle
- Department of Engineering and Natural Sciences, University of Applied Sciences Merseburg, 06217 Merseburg, Germany;
| | - Stefan Schwan
- Fraunhofer Institute for Microstructure of Materials and Systems IMWS, 06120 Halle/Saale, Germany;
| | - Lúcia Costa-Paiva
- Department of Obstetrics and Gynecology, School of Medical Sciences, University of Campinas—UNICAMP, Campinas, Sao Paulo 13083-887, Brazil;
| | - Munir S. Skaf
- Institute of Chemistry and Center for Computing in Engineering and Sciences, University of Campinas—UNICAMP, Campinas, Sao Paulo 13083-860, Brazil;
| | - Luiz C. Wrobel
- Institute of Materials and Manufacturing, Brunel University London, Uxbridge UB8 3PH, UK;
- Department of Civil and Environmental Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil
| | - Paulo Sollero
- Department of Computational Mechanics, School of Mechanical Engineering, University of Campinas—UNICAMP, Campinas, Sao Paulo 13083-860, Brazil; (A.C.S.A.); (D.P.)
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Diez-Perez A, Brandi ML, Al-Daghri N, Branco JC, Bruyère O, Cavalli L, Cooper C, Cortet B, Dawson-Hughes B, Dimai HP, Gonnelli S, Hadji P, Halbout P, Kaufman JM, Kurth A, Locquet M, Maggi S, Matijevic R, Reginster JY, Rizzoli R, Thierry T. 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). Aging Clin Exp Res 2019; 31:1375-1389. [PMID: 31422565 PMCID: PMC6763416 DOI: 10.1007/s40520-019-01294-4] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 07/24/2019] [Indexed: 01/19/2023]
Abstract
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.
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Affiliation(s)
- Adolfo Diez-Perez
- Department of Internal Medicine, Hospital del Mar/IMIM and CIBERFES, Autonomous University of Barcelona, Passeig Maritim 25-29, 08003, Barcelona, Spain.
| | - Maria Luisa Brandi
- FirmoLab Fondazione F.I.R.M.O., Florence, Italy
- Department of Biological, Experimental and Clinical Science, University of Florence, Florence, Italy
| | - Nasser Al-Daghri
- Chair for Biomarkers of Chronic Diseases, Biochemistry Department, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Jaime C Branco
- NOVA Medical School, Universidade Nova de Lisboa, Lisbon, Portugal
| | - Olivier Bruyère
- WHO Collaborating Centre for Public Health Aspects of Musculoskeletal Health and Aging, University of Liège, Liège, Belgium
| | - Loredana Cavalli
- FirmoLab Fondazione F.I.R.M.O., Florence, Italy
- Department of Biological, Experimental and Clinical Science, University of Florence, Florence, Italy
| | - Cyrus Cooper
- MRC Lifecourse Epidemiology Unit, Southampton General Hospital, University of Southampton, Southampton, UK
| | - Bernard Cortet
- Department of Rheumatology and EA 4490, University-Hospital of Lille, Lille, France
| | - Bess Dawson-Hughes
- Bone Metabolism Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA
| | - Hans Peter Dimai
- Department of Internal Medicine, Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Austria
| | - Stefano Gonnelli
- Department of Medicine, Surgery and Neurosciences, University of Siena, Siena, Italy
| | - Peyman Hadji
- Frankfurter Hormon und Osteoporose Zentrum, Frankfurt, Germany
| | | | - Jean-Marc Kaufman
- Department of Endocrinology, Ghent University Hospital, Ghent, Belgium
| | - Andreas Kurth
- Department of Orthopaedic Surgery and Osteology, Klinikum Frankfurt, Frankfurt, Germany
- Mayor Teaching Hospital, Charite Medical School, Berlin, Germany
| | - Medea Locquet
- Department of Public Health, Epidemiology and Health Economics, University of Liège, Liège, Belgium
| | - Stefania Maggi
- National Research Council, Aging Program, Institute of Neuroscience, Padua, Italy
| | - Radmila Matijevic
- Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia
- Clinical Center of Vojvodina, Clinic for Orthopedic Surgery, Novi Sad, Serbia
| | - Jean-Yves Reginster
- Chair for Biomarkers of Chronic Diseases, Biochemistry Department, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia
- WHO Collaborating Centre for Public Health Aspects of Musculoskeletal Health and Aging, University of Liège, Liège, Belgium
| | - René Rizzoli
- Service of Bone Diseases, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Thomas Thierry
- Department of Rheumatology, Hospital Nord, CHU St Etienne, St Etienne, France
- INSERM 1059, University of Lyon, St Etienne, France
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12
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Bhattacharya P, Altai Z, Qasim M, Viceconti M. A multiscale model to predict current absolute risk of femoral fracture in a postmenopausal population. Biomech Model Mechanobiol 2018; 18:301-318. [PMID: 30276488 PMCID: PMC6418062 DOI: 10.1007/s10237-018-1081-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Accepted: 09/24/2018] [Indexed: 02/06/2023]
Abstract
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.
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Affiliation(s)
- Pinaki Bhattacharya
- Department of Mechanical Engineering, University of Sheffield, The Sir Frederick Mappin Building, Mappin Street, Sheffield, S1 3JD, UK. .,INSIGNEO Institute for in Silico Medicine, University of Sheffield, The Pam Liversidge Building, Mappin Street, Sheffield, S1 3JD, UK.
| | - Zainab Altai
- Department of Mechanical Engineering, University of Sheffield, The Sir Frederick Mappin Building, Mappin Street, Sheffield, S1 3JD, UK.,INSIGNEO Institute for in Silico Medicine, University of Sheffield, The Pam Liversidge Building, Mappin Street, Sheffield, S1 3JD, UK
| | - Muhammad Qasim
- Department of Mechanical Engineering, University of Sheffield, The Sir Frederick Mappin Building, Mappin Street, Sheffield, S1 3JD, UK.,INSIGNEO Institute for in Silico Medicine, University of Sheffield, The Pam Liversidge Building, Mappin Street, Sheffield, S1 3JD, UK
| | - Marco Viceconti
- Department of Mechanical Engineering, University of Sheffield, The Sir Frederick Mappin Building, Mappin Street, Sheffield, S1 3JD, UK.,INSIGNEO Institute for in Silico Medicine, University of Sheffield, The Pam Liversidge Building, Mappin Street, Sheffield, S1 3JD, UK
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