Prediction of vertebral body mechanical parameters using opportunistic CT data
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References
- Hernlund E, Svedbom A, Ivergård M, et al. Osteoporosis in the European Union: medical management, epidemiology and economic burden. A report prepared in collaboration with the International Osteoporosis Foundation (IOF) and the European Federation of Pharmaceutical Industry Associations (EFPIA). Arch Osteoporos. 2013;8:136. https://doi.org/10.1007/s11657-013-0136-1
- Aibar-Almazán A, Voltes-Martínez A, Castellote-Caballero Y, et al. Current status of the diagnosis and management of osteoporosis. Int J Mol Sci. 2022;23:9465. https://doi.org/10.3390/ijms23169465
- Nethander M, Pettersson-Kymmer U, Vandenput L, et al. BMD-related genetic risk scores predict site-specific fractures as well as trabecular and cortical bone microstructure. J Clin Endocrinol Metab. 2020;105:e1344–e1357. https://doi.org/10.1210/clinem/dgaa082
- Shepherd JA, Schousboe JT, Broy SB, et al. Executive summary of the 2015 ISCD position development conference on advanced measures from DXA and QCT: fracture prediction beyond BMD. J Clin Densitom. 2015;18:274-286. https://doi.org/10.1016/j.jocd.2015.06.013
- Silva BC, Broy SB, Boutroy S, et al. Fracture risk prediction by non-BMD DXA measures: the 2015 ISCD official positions, part 2: trabecular bone score. J Clin Densitom. 2015;18:309-330. https://doi.org/10.1016/j.jocd.2015.06.008
- Adami G, Biffi A, Porcu G, et al. A systematic review on the performance of fracture risk assessment tools: FRAX, DeFRA, FRA-HS. J Endocrinol Invest. 2023;46:2287-2297. https://doi.org/10.1007/s40618-023-02082-8
- Soldati E, Rossi F, Vicente J, et al. Survey of MRI usefulness for the clinical assessment of bone microstructure. Int J Mol Sci. 2021;22:2509. https://doi.org/10.3390/ijms22052509
- Akhter MP, Recker RR. High resolution imaging in bone tissue research-review. Bone. 2021;143:115620. https://doi.org/10.1016/j.bone.2020.115620
- Johannesdottir F, Allaire B, Bouxsein ML. Fracture prediction by computed tomography and finite element analysis: current and future perspectives. Curr Osteoporos Rep. 2018;16:411-422. https://doi.org/10.1007/s11914-018-0450-z. Erratum in: Curr Osteoporos Rep. 2022;20:364. https://doi.org/10.1007/s11914-022-00724-z
- Fleps I, Morgan EF. A Review of CT-based fracture risk assessment with finite element modeling and machine learning. Curr Osteoporos Rep. 2022;20:309-319. https://doi.org/10.1007/s11914-022-00743-w
- Gebre RK, Hirvasniemi J, Lantto I, et al. Discrimination of low-energy acetabular fractures from controls using computed tomography-based bone characteristics. Ann Biomed Eng. 2021;49:367-381. https://doi.org/10.1007/s10439-020-02563-4
- Silva BC, Leslie WD, Resch H et al. Trabecular Bone Score: A noninvasive analytical method based upon the DXA image. J Bone Mineral Res. 2014;29:518-530. https://doi.org/10.1002/jbmr.3218
- López Picazo M, Humbert L, Di Gregorio S, et al. Discrimination of osteoporosis-related vertebral fractures by DXA-derived 3D measurements: a retrospective case-control study. Osteoporosis Int. 2019;30:1099-1110. https://doi.org/10.1007/s00198-019-04894-y
- Xie Q, Chen Y, Hu Y, et al. Development and validation of a machine learning-derived radiomics model for diagnosis of osteoporosis and osteopenia using quantitative computed tomography. BMC Med Imaging. 2022;22:140. https://doi.org/10.1186/s12880-022-00868-5
- Xue Z, Huo J, Sun X, et al. Using radiomic features of lumbar spine CT images to differentiate osteoporosis from normal bone density. BMC Musculoskelet Disord. 2022;23:336. https://doi.org/10:1186/s12891-022-05309-6
- Yan J, Lai Y, Xu Y, et al. Editorial: Artificial intelligence-based medical image automatic diagnosis and prognosis prediction. Front Phys. 2023;11:1210010. https://doi.org/10.3389/fphy.2023.1210010
- Valentinitsch A, Trebeschi S, Kaesmacher J, et al. Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures. Osteoporos Int. 2019;30:1275-1285. https://doi.org/10.1007/s00198-019-04910-1
- Leonhardt Y, May P, Gordijenko O, et al. Opportunistic QCT bone mineral density measurements predicting osteoporotic fractures: a use case in a prospective clinical cohort. Front Endocrinol. 2020;11:586352. https://doi.org/10.3389/fendo.2020.586352
- Löffler MT, Jacob A, Valentinitsch A, et al. Improved prediction of incident vertebral fractures using opportunistic QCT compared to DXA. Eur Radiol. 2019;29:4980-4989. https://doi.org/10.1007/s00330-019-0618-w
- Boutin RD, Hernandez AM, Lenchik L, et al. CT phantom evaluation of 67,392 American College of Radiology accreditation examinations: implications for opportunistic screening of osteoporosis using CT. Am J Roentgenol. 2021;216:447-452. https://doi.org/10.2214/AJR.20.22943
- Lenchik L, Weaver AA, Ward RJ, et al. Opportunistic screening for osteoporosis using computed tomography: State of the art and argument for paradigm shift. Curr Rheumatol Rep. 2018;20:74. https://doi.org/10.1007/s11926-018-0784-7
- Tatoń G, Rokita E, Rok T, et al. Oversampling in the computed tomography measurements applied for bone structure studies as a method of spatial resolution improvement. Pol J Radiol. 2012;77:14-18. https://doi.org/10.12659/pjr.882965
- Tatoń G, Rokita E, Wróbel A. Application of geometrical measurements in the assessment of vertebral strength. Pol J Radiol. 2013;78:15-18. https://doi.org/10.12659/PJR.883942
- Tatoń G, Rokita E, Wróbel et al. Combining areal DXA bone mineral density and vertebrae postero-anterior width improves the prediction of vertebral strength. Skeletal Radiol. 2013;42:1717-1725. https://doi.org/10.1007/s00256-013-1723-3
- Tatoń G, Rokita E, Korkosz et al. The ratio of anterior and posterior vertebral heights reinforces the utility of DXA in assessment of vertebrae strength. Calcif Tissue Int. 2014;95:112-121. https://doi.org/10.1007/s00223-014-9868-1
- Alswat KA. Gender disparities in osteoporosis. J Clin Med Res. 2017;9:382-387. https://doi.org/10.14740/jocmr2970w
- Genant HK, Wu CY, Kuijk C, et al. Vertebral fracture assessment using a semiquantitative technique. J Bone Miner Res. 1993;8:1137-1148. https://doi.org/10.1002/jbmr.5650080915
- Steiger JH. Tests for comparing elements of a correlation matrix. Psychol Bull. 1980;187:245-251. https://doi.org/10.1037/0033-2909.87.2.245
- Tabor Z, Rokita E. Comparison of trabecular bone architecture in young and old bone. Med Phys. 2000;27:765-772. https://doi.org/10.1118/1.598981
- Kubik T, Pasowicz M, Tabor Z, et al. Optimizing the assessment of age-related changes in trabecular bone. Phys Med Biol. 2002;47:1543-1553. https://doi.org/10.1088/0031-9155/47/9/309
- Tabor Z. Quantifying quality of trabecular bone from low-resolution images. Nalecz Institute of Bio-cybernetics and Biomedical Engineering Polish Academy of Science, Warsaw, 2009; 16-56.
- Karim L, Hussein AI, Morgan EF, et al. The mechanical behavior of bone. In: Marcus R, Feldman D, Dempster DW, Luckey M, Cauley JA, editors. Osteoporosis. Oxford: Academic Press; 2013. p. 431-452. https://doi.org/10.1016/B978-0-12-415853-5.00019-4
- Boskey AL, Imbert L. Bone quality changes associated with aging and disease: a review. Ann N Y Acad Sci. 2017;1410:93-106. https://doi.org/10.1111/nyas.13937
- Jain RK, Vokes T. Dual-energy X-ray absorptiometry. J Clin Densitometry. 2017;20:291-303. https://doi.org/10.1016/j.jocd.2017.06.014
- American College of Radiology. ACR-SPR-SSR practice guideline the performance of quantitative computed tomography (QCT) bone densitometry. 2013. Available at: https://www.acr.org/-/media/ACR/Files/Practice-Parameters/QCT.pdf?la=en (Accessed 12 April 2024).
- Yamada S, Chiba K, Okazaki N, et al. Correlation between vertebral bone microstructure and estimated strength in elderly women: an ex-vivo HR-pQCT study of cadaveric spine. Bone. 2019;120:459-464. https://doi.org/10.1016/j.bone.2018.12.005
- Liu Y, Wang L, Su Y, et al. CTXA hip: the effect of partial volume correction on volumetric bone mineral density data for cortical and trabecular bone. Arch Osteoporos. 2020;15:50. https://doi.org/10.1007/s11657-020-00721-8
- Engelke K. Quantitative computed tomography – current status and new developments. J Clin Densitom. 2017;20:309-321. https://doi.org/10.1016/j.jocd.2017.06.017
- Checefsky WA, Abidin AZ, Nagarajan MB, et al. Assessing vertebral fracture risk on volumetric quantitative computed tomography by geometric characterization of trabecular bone structure. Proc SPIE - Int Soc Opt Eng. 2016;9785:978508. https://doi.org/10.1117/12.2216898
- Lee DC, Hoffmann PF, Kopperdahl DL, et al. Phantomless calibration of CT scans for measurement of BMD and bone strength-inter-operator reanalysis precision. Bone. 2017;103:325-33. https://doi.org/10.1016/j.bone.2017.07.029
- Gibson LJ. Biomechanics of cellular solid. J Biomech. 2005;38:377-399. https://doi.org/10.1016/j.jbiomech.2004.09.027
- Moeendarbary E, Harris AR. Cell mechanics: principles, practices, and prospects. Rev Syst Biol Med. 2014;6:371-388. https://doi.org/10.1002/wsbm.1275
- Costanza G, Solaiyappan D, Tata ME. Properties, applications and recent developments of cellular solid materials: A review. Materials. 2023;16:7076. https://doi.org/10.3390/ma16227076
- Coulombe JC, Mullen ZK, Lynch ME, et al. Application of machine learning classifiers for microcomputed tomography data assessment of mouse bone microarchitecture. MethodsX. 2021;8:101497. https://doi.org/10.1016/jmex.2021.101497
- Kodama M, Takeuchi A, Uesugi M, at al. Machine learning super-resolution of laboratory CT images in all-solid-state batteries using synchrotron radiation CT as training data. Energy and AI. 2023;100305. https://doi.org/10.1016/j.egyai.2023.100305
Language: English
Submitted on: Aug 1, 2024
Accepted on: Dec 3, 2024
Published on: Dec 23, 2024
Published by: Polish Society of Medical Physics
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
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© 2024 Eugeniusz Rokita, Grzegorz Tatoń, published by Polish Society of Medical Physics
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