1
|
Grassi L, Väänänen SP, Jehpsson L, Ljunggren Ö, Rosengren BE, Karlsson MK, Isaksson H. 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. J Bone Miner Res 2023; 38:1258-1267. [PMID: 37417707 DOI: 10.1002/jbmr.4878] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 06/15/2023] [Accepted: 07/04/2023] [Indexed: 07/08/2023]
Abstract
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).
Collapse
Affiliation(s)
- Lorenzo Grassi
- Department of Biomedical Engineering, Lund University, Lund, Sweden
| | - Sami P Väänänen
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
- Department of Applied Physics, University of Eastern Finland, Eastern Finland, Finland
| | - Lars Jehpsson
- Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Östen Ljunggren
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Björn E Rosengren
- Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Magnus K Karlsson
- Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Hanna Isaksson
- Department of Biomedical Engineering, Lund University, Lund, Sweden
| |
Collapse
|
2
|
Foessl I, Bassett JHD, Bjørnerem Å, Busse B, Calado Â, Chavassieux P, Christou M, Douni E, Fiedler IAK, Fonseca JE, Hassler E, Högler W, Kague E, Karasik D, Khashayar P, Langdahl BL, Leitch VD, Lopes P, Markozannes G, McGuigan FEA, Medina-Gomez C, Ntzani E, Oei L, Ohlsson C, Szulc P, Tobias JH, Trajanoska K, Tuzun Ş, Valjevac A, van Rietbergen B, Williams GR, Zekic T, Rivadeneira F, Obermayer-Pietsch B. Bone Phenotyping Approaches in Human, Mice and Zebrafish - Expert Overview of the EU Cost Action GEMSTONE ("GEnomics of MusculoSkeletal traits TranslatiOnal NEtwork"). Front Endocrinol (Lausanne) 2021; 12:720728. [PMID: 34925226 PMCID: PMC8672201 DOI: 10.3389/fendo.2021.720728] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 10/21/2021] [Indexed: 12/16/2022] Open
Abstract
A synoptic overview of scientific methods applied in bone and associated research fields across species has yet to be published. Experts from the EU Cost Action GEMSTONE ("GEnomics of MusculoSkeletal Traits translational Network") Working Group 2 present an overview of the routine techniques as well as clinical and research approaches employed to characterize bone phenotypes in humans and selected animal models (mice and zebrafish) of health and disease. The goal is consolidation of knowledge and a map for future research. This expert paper provides a comprehensive overview of state-of-the-art technologies to investigate bone properties in humans and animals - including their strengths and weaknesses. New research methodologies are outlined and future strategies are discussed to combine phenotypic with rapidly developing -omics data in order to advance musculoskeletal research and move towards "personalised medicine".
Collapse
Affiliation(s)
- Ines Foessl
- Department of Internal Medicine, Division of Endocrinology and Diabetology, Endocrine Lab Platform, Medical University of Graz, Graz, Austria
| | - J. H. Duncan Bassett
- Molecular Endocrinology Laboratory, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Åshild Bjørnerem
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromsø, Norway
- Norwegian Research Centre for Women’s Health, Oslo University Hospital, Oslo, Norway
| | - Björn Busse
- Department of Osteology and Biomechanics, University Medical Center, Hamburg-Eppendorf, Hamburg, Germany
| | - Ângelo Calado
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Centro Académico de Medicina de Lisboa, Lisboa, Portugal
| | | | - Maria Christou
- Department of Hygiene and Epidemiology, Medical School, University of Ioannina, Ioannina, Greece
| | - Eleni Douni
- Institute for Bioinnovation, Biomedical Sciences Research Center “Alexander Fleming”, Vari, Greece
- Department of Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Imke A. K. Fiedler
- Department of Osteology and Biomechanics, University Medical Center, Hamburg-Eppendorf, Hamburg, Germany
| | - João Eurico Fonseca
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Centro Académico de Medicina de Lisboa, Lisboa, Portugal
- Rheumatology Department, Hospital de Santa Maria, Centro Hospitalar Universitário Lisboa Norte (CHULN), Lisbon Academic Medical Centre, Lisbon, Portugal
| | - Eva Hassler
- Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology, Medical University Graz, Graz, Austria
| | - Wolfgang Högler
- Department of Paediatrics and Adolescent Medicine, Johannes Kepler University Linz, Linz, Austria
| | - Erika Kague
- The School of Physiology, Pharmacology and Neuroscience, Biomedical Sciences, University of Bristol, Bristol, United Kingdom
| | - David Karasik
- Azrieli Faculty of Medicine, Bar-Ilan University, Ramat Gan, Israel
| | - Patricia Khashayar
- Center for Microsystems Technology, Imec and Ghent University, Ghent, Belgium
| | - Bente L. Langdahl
- Department of Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - Victoria D. Leitch
- Innovative Manufacturing Cooperative Research Centre, Royal Melbourne Institute of Technology, School of Engineering, Carlton, VIC, Australia
| | - Philippe Lopes
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Georgios Markozannes
- Department of Hygiene and Epidemiology, Medical School, University of Ioannina, Ioannina, Greece
| | | | | | - Evangelia Ntzani
- Department of Hygiene and Epidemiology, Medical School, University of Ioannina, Ioannina, Greece
- Department of Health Services, Policy and Practice, Center for Research Synthesis in Health, School of Public Health, Brown University, Providence, RI, United States
| | - Ling Oei
- Centre for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Claes Ohlsson
- Centre for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Department of Drug Treatment, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Pawel Szulc
- INSERM UMR 1033, University of Lyon, Lyon, France
| | - Jonathan H. Tobias
- Musculoskeletal Research Unit, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit, Bristol Medical School, Bristol, University of Bristol, Bristol, United Kingdom
| | - Katerina Trajanoska
- Department of Internal Medicine, Erasmus MC Rotterdam, Rotterdam, Netherlands
| | - Şansın Tuzun
- Physical Medicine & Rehabilitation Department, Cerrahpasa Medical Faculty, Istanbul University-Cerrahpaşa, Istanbul, Turkey
| | - Amina Valjevac
- Department of Human Physiology, School of Medicine, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Bert van Rietbergen
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Graham R. Williams
- Molecular Endocrinology Laboratory, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Tatjana Zekic
- Department of Rheumatology and Clinical Immunology, Faculty of Medicine, Clinical Hospital Center Rijeka, Rijeka, Croatia
| | | | - Barbara Obermayer-Pietsch
- Department of Internal Medicine, Division of Endocrinology and Diabetology, Endocrine Lab Platform, Medical University of Graz, Graz, Austria
| |
Collapse
|
3
|
Aggarwal V, Maslen C, Abel RL, Bhattacharya P, Bromiley PA, Clark EM, Compston JE, Crabtree N, Gregory JS, Kariki EP, Harvey NC, Ward KA, Poole KES. Opportunistic diagnosis of osteoporosis, fragile bone strength and vertebral fractures from routine CT scans; a review of approved technology systems and pathways to implementation. Ther Adv Musculoskelet Dis 2021; 13:1759720X211024029. [PMID: 34290831 PMCID: PMC8274099 DOI: 10.1177/1759720x211024029] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 05/18/2021] [Indexed: 12/21/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Veena Aggarwal
- Kingston Hospital NHS Foundation Trust, Kingston Upon Thames, UK
| | | | | | | | | | | | | | - Nicola Crabtree
- Birmingham Women’s and Children’s NHS Foundation Trust, Birmingham, UK
| | - Jennifer S. Gregory
- University of Aberdeen School of Medicine Medical Sciences and Nutrition, Aberdeen, UK
| | | | | | - Kate A. Ward
- University of Southampton, Southampton, Hampshire, UK
| | - Kenneth E. S. Poole
- University of Cambridge School of Clinical Medicine, Addenbrooke’s Hospital, NIHR Cambridge Biomedical Research Centre, Cambridge, CB2 0QQ, UK
| |
Collapse
|
5
|
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.
Collapse
Affiliation(s)
- Cristina Falcinelli
- Orthopaedic Biomechanics Laboratory, Sunnybrook Research Institute, Toronto, Canada
| | - Cari Whyne
- Orthopaedic Biomechanics Laboratory, Sunnybrook Research Institute, Toronto, Canada
| |
Collapse
|