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short-paper

3D bone microarchitecture modeling and fracture risk prediction

Authors:

Murali Ramanathan,

Aidong ZhangAuthors Info & Claims

BCB '12: Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine

Pages 361 - 368

https://doi.org/10.1145/2382936.2382982

Published: 07 October 2012 Publication History

Abstract

As prevalence and awareness of osteoporosis increase and treatments of proven efficacy become available, the demand for management of patients with the disease will also rise. It calls for innovative research on understanding of osteoporosis and fracture mechanisms, allowing early and more accurate prediction of bone disease progression. The most widely validated technique for the diagnosis of osteoporosis is Bone Mineral Density (BMD) measurement based on dual energy X-ray absorptiometry (DXA). However, a major limitation of BMD is that it incompletely reflects the variation in bone strength. In this paper we develop and evaluate a novel three-dimensional (3D) computational bone framework capable of providing: (1) Spatio-temporal 3D microstructure bone model; (2) Derived quantitative measures of 3D bone microarchitecture; (3) Analysis of BMD and bone strength; and (4) A state-of-the-art probabilistic approach to analyze bone fracture risk factors including demographic attributes and life styles. Beyond efficient 3D bone microstructure representation, quantitative assessment is considered not only for identifying critical elements in bone microstructure, but also ensuring effective predictioin of bone diseases in advance. The simulation network model of 3D bone microarchitecture and extensive empirical study on fracture risk improve our understanding of bone disease risk arising from the complex interplay of the human BMD assessment result with presence of major risk factors.

References

[1]

http://www.shef.ac.uk/FRAX/.

[2]

http://www.fractal.org/.

[3]

T. G. Smith, Jr., G. D. Lange and W. B. Marks. Fractal methods and results in cellular morphology. Journal of Neuroscience Methods, 69:1123--126, 1996.

[4]

Ammann, P. and Rizzoli, R. Bone strength and its determinants. Osteoporos Int, 14:13--18, 2003.

[5]

Aurenhammer, F. Voronoi diagrams - a survey of a fundamental geometric data structure. ACM Computing Surveys, 23:345--405, 1991.

Digital Library

[6]

Barnes, C., Newall, F., Ignjatovic, V., Wong, P., Cameron, F., Jones, G., and Monagle, P. Reduced bone density in children on long-term warfarin. Pediatric Research, 57:578--581, 2005.

[7]

Bock, M., Tyagi, A. K., Kreft, J. U., and Alt, W. Generalized voronoi tessellation as a model of two-dimensional cell tissue dynamics. Biological Physics, 0901:4469, 2009.

[8]

Cummings, S. R., Nevitt, M. C., Browner, W. S., Stone, K., Fox, K. M., Ensrud, K. E., Cauley, J., Black, D., and Vogt, T. M. Risk factors for hip fracture in white women. Study of Osteoporotic fractures research group, 332:767--773, 1995.

[9]

Doube M, Klosowski MM, Arganda-Carreras I, Cordelieres F, Dougherty RP, Jackson J, Schmid B, Hutchinson JR, Shefelbine SJ. Bonej: free and extensible bone image analysis in imagej. Bone, 47:1076--1079, 2010.

[10]

European Foundation for Osteoporosis. Consensus development conference: Prophylaxis and treatment of osteoporosis. Osteonorosis International, 1:114--117, 1991.

[11]

Garnero, P., Delmas, P. D. Noninvasive techniques for assessing skeletal changes in inflammatory arthritis: bone biomarkers. Curr Opin Rheumatol., 16:428--434, 2004.

[12]

Goodhand, J. R. and Kamperidis, N. and Nguyen, H. and Wahed, M. and Rampton, D. S. Application of the who fracture risk assessment tool (frax) to predict need for dexa scanning and treatment in patients with inflammatory bowel disease at risk of osteoporosis. Alimentary Pharmacology and Therapeutics, 33(5):551--558, 2011.

[13]

Hui, S. L., Slemenda, C. W. and Johnston, C. C. Age and bone mass as predictors of fracture in a prospective study. The Journal of Clinical Investigation, 81:1804--1809, 1988.

[14]

Johnell, O., Kanis, J. A., Oden, A., Johansson, H., De Laet, C., Delmas, P., Eisman, J. A., Fujiwara, S., Kroger, H., Mellstrom, D., Meunier, P. J., Melton, L. J. 3rd, O'Neill, T., Pols, H., Reeve, J., Silman, A., and Tenenhouse, A. Predictive value of bmd for hip and other fractures. Journal of bone and mineral research, 20:1185--94, 2005.

[15]

Johnell, Olof and Nilsson, Bo. Life-style and bone mineral mass in perimenopausal women. Calcified Tissue International, 36:354--356, 1984.

[16]

Kanis, J. A., Borgstrom, F., De Laet, C., Johansson, H., Johnell, O., Jonsson, B., Oden, A., Zethraeus, N., Pfleger, B., and Khaltaev, N. Assessment of fracture risk. Osteoporos Int, 164:581--589, 2005.

[17]

Kanis JA, Oden A, Johansson H, Borgstrom F, Strom O, McCloskey E. Frax and its applications to clinical practice. Bone., 44:734--743, 2009.

[18]

Kim, T., Bone, L., Ramanathan, M. and Zhang, A. Mathematical network model for bone mineral density (bmd) and bone quality assessment. In the ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM BCB), pages 69--75, 2011.

Digital Library

[19]

Kim, T., Hwang, W., Ramanathan, M., and Zhang, A. Computational framework for microstructural bone dynamics model and its evaluation. In IEEE International Conference on Bioinformatics and Bioengineering (BIBE), pages 92--98, 2010.

Digital Library

[20]

Kim, T., Hwang, W., Zhang, A., Ramanathan, M. and Sen, S. Damage isolation via strategic self-destruction: A case study in 2d random networks. Europhysics Letters, 2:77, 2009.

[21]

Kim, T., Koh, J., Li, K., Ramanathan, M. and Zhang, A. Identification of critical location on a microstructural bone network. In IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 557--562, 2010.

[22]

Kim, T., Ramanathan, M. and Zhang, A. A graph-based approach for computational model of bone microstructure. In ACM Workshop on IWBNA, in conjunction with ACM International Conference Bioinformatics and Computational Biology (ACM-BCB), pages 563--568, 2010.

Digital Library

[23]

Kim, T., Ramanathan, M., and Zhang, A. Bonenet: A network model of bone microstructure and dynamics. International Journal of Data Mining and Bioinformatics (IJDMB), accepted.

[24]

Krall, Elizabeth A. and Dawson-Hughes, Bess. Heritable and life-style determinants of bone mineral density. Journal of Bone and Mineral Research, 8(1):1--9, 1993.

[25]

L. Pothuaud, C. L. Benhamou, P. Porion, E. Lespessailles, R. Harba, P. Levitz M. D. Fractal dimension of trabecular bone projection texture is related to three-dimensional microarchitecture. Journal of Bone and Mineral Research, 15:691--696, 2000.

[26]

Nelder, J. A. and R. Mead. A simplex method for function minimization. Computer Journal, 7:308--313, 1965.

[27]

Ott, S. M., Kilcoyne, R. F., and Chesnut, C. H. 3rd. Ability of four different techniques of measuring bone mass to diagnose vertebral fractures in postmenopausal women. Bone Miner Res, 2:201--210, 1987.

[28]

Pludowski, P., Lebiedowski, M., and Lorenc, R. S. Dual energy x-ray absorptiometry. Osteoporos Int, 14:317--322, 2004.

[29]

Taylor, B. C., Schreiner, P. J., Stone, K. L., Fink, H. A., Cummings, S. R., Nevitt, M. C., Bowman, P. J., and Ensrud, K. E. Long-term prediction of incident hip fracture risk in elderly white women: study of osteoporotic fractures. J Am Geriatr Soc Int, 52:1479--1486, 2004.

Cited By

Li HLi XJia XRamanathan MZhang A(2015)Bone disease prediction and phenotype discovery using feature representation over electronic health recordsProceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/2808719.2808741(212-221)Online publication date: 9-Sep-2015
https://dl.acm.org/doi/10.1145/2808719.2808741
Li HLi XRamanathan MZhang A(2015)Prediction and informative risk factor selection of bone diseasesIEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2014.233057912:1(79-91)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1109/TCBB.2014.2330579
Li HLi XRamanathan MZhang A(2013)A Semi-Supervised Learning Approach to Integrated Salient Risk Features for Bone DiseasesProceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics10.1145/2506583.2506593(42-51)Online publication date: 22-Sep-2013
https://dl.acm.org/doi/10.1145/2506583.2506593
Show More Cited By

Index Terms

3D bone microarchitecture modeling and fracture risk prediction

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cover image ACM Conferences

BCB '12: Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine

October 2012

725 pages

ISBN:9781450316705

DOI:10.1145/2382936

General Chair:
Sanjay Ranka
University of Florida
,
Program Chairs:
Tamer Kahveci
University of Florida
,
Mona Singh
Princeton University

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGBio: ACM Special Interest Group on Bioinformatics

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 October 2012

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BCB' 12

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SIGBio

BCB' 12: ACM International Conference on Bioinformatics, Computational Biology and Biomedicine

October 7 - 10, 2012

Florida, Orlando

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BCB '12 Paper Acceptance Rate 33 of 159 submissions, 21%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Li HLi XJia XRamanathan MZhang A(2015)Bone disease prediction and phenotype discovery using feature representation over electronic health recordsProceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/2808719.2808741(212-221)Online publication date: 9-Sep-2015
https://dl.acm.org/doi/10.1145/2808719.2808741
Li HLi XRamanathan MZhang A(2015)Prediction and informative risk factor selection of bone diseasesIEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2014.233057912:1(79-91)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1109/TCBB.2014.2330579
Li HLi XRamanathan MZhang A(2013)A Semi-Supervised Learning Approach to Integrated Salient Risk Features for Bone DiseasesProceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics10.1145/2506583.2506593(42-51)Online publication date: 22-Sep-2013
https://dl.acm.org/doi/10.1145/2506583.2506593
Li HLi XZhang YRamanathan MZhang A(2013)A generative framework for prediction and informative risk factor selection of bone diseases2013 IEEE International Conference on Bioinformatics and Biomedicine10.1109/BIBM.2013.6732557(554-559)Online publication date: Dec-2013
https://doi.org/10.1109/BIBM.2013.6732557

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