Osteoporosis is a skeletal disorder characterized by compromised bone strength predisposing a person to an increased risk of fracture. In the U.S. today, 10 million individuals are estimated to already have the disease and almost 34 million more are estimated to have low bone density, placing them at increased risk for osteoporosis and broken bones. Currently, determination of fracture risk, aging effects, and therapeutic efficacy is primarily based on bone mineral density (BMD) measured by areal or volumetric X-ray-based imaging techniques. BMD can predict bone strength and fracture risk to some extent, however, studies have shown that BMD only explains about 70%-75% of the variance in strength, while the remaining variance has been attributed to the cumulative and synergistic effect of other factors such as bone structure, topology, geometry, tissue composition, microdamage, and biomechanical factors. High-resolution peripheral quantitative computed tomography (HR-pQCT) is a noninvasive in-vivo imaging technique which depicts many of these features, including density, geometry, structure, topology, and mechanics of cortical and trabecular bone in the distal radius and distal tibia. To date HR-pQCT imagery has been analyzed using conventional quantitative approaches that average bone features over large regions of interest. The individual quantification of average bone features (uni-parametric) or their statistical combination (multi-parametric) disregard how these three-dimensional (3D) features synergistically contribute to bone strength. As a result the traditional methods fail to capture the spatial patterning of the effect being studied, which is key to understanding the underlying biology. Bone is a 3D organ experiencing constant adaptation through remodeling, and should therefore be analyzed with 3D techniques that reflect the complementary and interdependent nature of different bone features. Statistical parametric mapping (SPM) is a technique that enables 3D spatial comparisons of multi-parametric maps between groups of subjects. Instead of measuring summary properties for arbitrary or subjective volumes of interest, this data-driven process identifies regions significantly associated with a variable of interest through valid statistical tests, thus generating 3D statistical and P-value maps that facilitate the visualization and consequently the interpretation of comparisons between target populations. The ultimate goal of this proposal is to establish a framework to automatically identify relevant bone sub-regions and features in specific populations for the targeted quantitative assessment of the spatial distribution and prediction of bone strength using HR-pQCT. For this purpose, specialized SPM techniques have been developed for HR-pQCT. To evaluate the potential of SPM in clinical science, we propose to apply SPM to image data from three existing in-vivo HR-pQCT studies investigating: a) regional variations in bone structure related to gender and age; b) differences due to fracture of the forearm; and c) longitudinal effects of two osteoporosis treatments.
We propose a population-based framework to automatically identify relevant bone sub-regions and features in specific populations for the targeted quantitative assessment of the spatial distribution and prediction of bone strength using HR-pQCT. To demonstrate the potential of this framework in clinical science, we apply it to existing HR-pQCT studies to identify bone sub-regions and features significantly associated with age, gender, fracture status and response to osteoporosis treatment in post menopausal women; identify spatial associations between the central and distal skeleton with respect to treatment response; and improve fracture discrimination, and the prediction and understanding of the effects of osteoporosis treatment. This framework could improve the development of innovative, more active and safer drugs and therapies, and directly benefit patients suffering osteoporosis and other bone disorders since based on HR-pQCT maps of parameters estimating bone density and quality, a treatment offering the most clinical benefits to them could be prescribed.
| Yu, Aihong; Carballido-Gamio, Julio; Wang, Ling et al. (2017) Spatial Differences in the Distribution of Bone Between Femoral Neck and Trochanteric Fractures. J Bone Miner Res 32:1672-1680 |
| Carballido-Gamio, Julio; Bonaretti, Serena; Kazakia, Galateia J et al. (2017) Statistical Parametric Mapping of HR-pQCT Images: A Tool for Population-Based Local Comparisons of Micro-Scale Bone Features. Ann Biomed Eng 45:949-962 |
