Boyd, Steven KyleSchneider, Prism SteorraWhittier, Danielle Elizabeth Wein2021-09-162021-09-162021-08Whittier, D. E. W. (2021). The assessment of fragility fracture risk using HR-pQCT as a novel tool for diagnosis of osteoporosis (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/113877Osteoporosis is a systemic skeletal disease, characterized by reduced bone density and deterioration of bone microarchitecture, leading to increased fracture risk. However, current diagnosis using dual-energy X-ray absorptiometry (DXA) only accounts for density and consequently fails to capture most individuals who fracture. High-resolution peripheral quantitative computed tomography (HR-pQCT) is a medical imaging modality capable of characterizing three-dimensional bone microarchitecture at peripheral skeletal sites, and has demonstrated that bone microarchitecture can improve prediction of fracture risk. However, to date the improvement is modest, as interpretation of the interaction between fracture and the numerous parameters provided by HR-pQCT is complex. The objective of this dissertation was to elucidate the key microarchitectural characteristics that underpin bone fragility, and use these insights to improve assessment of fracture risk with HR-pQCT. First, reference data in the form of centile curves was established for HR-pQCT parameters using a population-based cohort (n=1,236, age 18–90 years), and a new intuitive parameter called void space was developed to capture localized regions of bone loss in HR-pQCT images. In a separate prospective multi-center cohort (n=5,873, age 40–90 years), unsupervised machine learning was implemented to identify common groupings (i.e., phenotypes) of bone microarchitecture in older adults. Three phenotypes were identified and characterized as low density, structurally impaired, and healthy bone, where the low density phenotype had the strongest association with incident osteoporotic fractures (hazard ratio = 3.28). Using the same cohort, a fracture risk assessment tool, called µFRAC, was developed using supervised machine learning methods to provide a 5-year risk of major osteoporotic fracture based on HR-pQCT parameters, and was demonstrated to significantly outperform DXA in predicting fracture risk. Finally, a new retrospective cohort of patients with fragility fractures at the hip (n=108, age 56–96 years) was used to characterize bone fragility. Hip fracture patients were significantly associated with the low density phenotype and had bone void spaces that were 2–3 times larger than controls. Together, these findings provide insight into the characteristics of bone that lead to osteoporotic fractures and introduces tools that enable insightful interpretation of HR-pQCT data for clinical use.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Medical ImagingHR-pQCTBone MorphologyMachine LearningOsteoporosisFracture PredictionEngineering--Biomedicaldoctoral thesis10.11575/PRISM/39203