title: Improving bone strength prediction in human proximal femur specimens through geometrical characterization of trabecular bone microarchitecture and support vector regression.
abstract: We investigate the use of different trabecular bone descriptors and advanced machine learning tech niques to complement standard bone mineral density (BMD) measures derived from dual-energy x-ray absorptiometry (DXA) for improving clinical assessment of osteoporotic fracture risk. For this purpose, volumes of interest were extracted from the head, neck, and trochanter of 146ex vivoproximal femur specimens on multidetector computer tomography. The trabecular bone captured was characterized with (1) statistical moments of the BMD distribution, (2) geometrical features derived from the scaling index method (SIM), and (3) morphometric parameters, such as bone fraction, trabecular thickness, etc. Feature sets comprising DXA BMD and such supplemental features were used to predict the failure load (FL) of the specimens, previously determined through biomechanical testing, with multiregression and support vector regression. Prediction performance was measured by the root mean square error (RMSE); correlation with measured FL was evaluated using the coefficient of determinationR2. The best prediction performance was achieved by a combination of DXA BMD and SIM-derived geometric features derived from the femoral head (RMSE: 0.869 ± 0.121,R2: 0.68 ± 0.079), which was significantly better than DXA BMD alone (RMSE: 0.948 ± 0.119,R2: 0.61 ± 0.101) (p< 10-4). For multivariate feature sets, SVR outperformed multiregression (p< 0.05). These results suggest that supplementing standard DXA BMD measurements with sophisticated femoral trabecular bone characterization and supervised learning techniques can significantly improve biomechanical strength prediction in proximal femur specimens.
authors.affiliation: University of Rochester, Departments of Imaging Sciences and Biomedical Engineering, Rochester, New York 14627.
authors.forename: Chien-Chun
authors.initials: CC
authors.lastname: Yang
authors.affiliation: University of Rochester, Departments of Imaging Sciences and Biomedical Engineering, Rochester, New York 14627.
authors.forename: Mahesh B
authors.initials: MB
authors.lastname: Nagarajan
authors.affiliation: University of Rochester, Departments of Imaging Sciences and Biomedical Engineering, Rochester, New York 14627.
authors.forename: Markus B
authors.initials: MB
authors.lastname: Huber
authors.affiliation: University of California San Francisco, Musculoskeletal and Quantitative Imaging Research, Department of Radiology and Biomedical Imaging, San Francisco, California 94143.
authors.forename: Julio
authors.initials: J
authors.lastname: Carballido-Gamio
authors.affiliation: Technische Universität München, Institut Für Röntgendiagnostik, Munich, München 85748, Germany.
authors.forename: Jan S
authors.initials: JS
authors.lastname: Bauer
authors.affiliation: Technische Universität München, Institut Für Röntgendiagnostik, Munich, München 85748, Germany.
authors.forename: Thomas
authors.initials: T
authors.lastname: Baum
authors.affiliation: Paracelsus Medical University Salzburg, Institute of Anatomy and Musculoskeletal Research, Salzburg 5020, Austria.
authors.forename: Felix
authors.initials: F
authors.lastname: Eckstein
authors.affiliation: Paracelsus Medical University Salzburg, Institute of Anatomy and Musculoskeletal Research, Salzburg 5020, Austria.
authors.forename: Eva
authors.initials: E
authors.lastname: Lochmüller
authors.affiliation: University of California San Francisco, Musculoskeletal and Quantitative Imaging Research, Department of Radiology and Biomedical Imaging, San Francisco, California 94143.
authors.forename: Sharmila
authors.initials: S
authors.lastname: Majumdar
authors.affiliation: University of California San Francisco, Musculoskeletal and Quantitative Imaging Research, Department of Radiology and Biomedical Imaging, San Francisco, California 94143.
authors.forename: Thomas M
authors.initials: TM
authors.lastname: Link
authors.affiliation: University of Rochester, Departments of Imaging Sciences and Biomedical Engineering, Rochester, New York 14627.|University of Munich, Department of Radiology, München 80539, Germany.
authors.forename: Axel
authors.initials: A
authors.lastname: Wismüller
keywords: bone mineral density; dual x-ray absorptiometry; osteoporosis; quantitative computer tomography; scaling index method; support vector regression; trabecular bone