Bone fractures are a global public health issue and incidence rates continue to increase with our aging society. While most fractures heal without any complications, up to 10% of fractures exhibit delayed healing, referred to as non-union. These cases often lead to expensive and painful secondary interventions. Current knowledge of fracture healing is based on experimental studies using animal models and focuses on the midshaft of long bones (i.e. femur diaphysis), which is made up of a dense shell of cortical bone filled with bone marrow. However, the majority of bone fractures occur in the distal segment long bones, known as the metaphysis, or within the vertebral body, both made up of a marrow-filled network of trabecular bone and a thin cortical shell. The bone microstructure in these locations is vastly different, heavily impacting the nature of bone fracture healing in these regions.
Bone is a mechanosensetive tissue, meaning it responds to loading. Local mechanical tissue loading is a major regulator of the cellular activities in the healing process and might provide a unique therapeutic target for addressing non-unions. However, existing tools for the assessment of bone fracture healing lack the resolution to identify and isolate the initial fracture sites, especially if these occur in the metaphysis. Further, these tools are unable to provide detailed information on healing progression at the microstructural level. With the advent of high-resolution peripheral quantitative computed tomography (HR-pQCT), it is now possible to assess changes in microstructural and biomechanical properties in vivo. Further, medical image-based computational modeling (i.e. micro-finite element analysis (µFE)) has been shown to be a powerful, non-invasive tool for monitoring bone fracture and predicting the local bone remodelling response in both animal and human studies. Although such in silico approaches have become a popular laboratory method for predicting bone remodelling and fracture risk, it has yet to be validated for healing bone in humans. Such analyses could serve as a new tool for fracture assessment and may provide quantitative guidance for clinicians.
The aim of the EU-funded HealinguFE project was to develop a tool for identifying metaphyseal fractures and predicting their healing evolution. Using HR-pQCT data from patients with wrist fractures, scientists sought to develop an in silico model capable of detecting structural changes in the bone over time in order to advance existing knowledge on the mechanisms underlying trabecular bone healing and fuel future research towards novel interventions. During the project, a series of image processing tools and a robust µFE pipeline for assessing time-lapsed fracture healing in vivo were developed. Using these tools, drastically delayed fracture healing was observed in older patients and the µFE-derived mechanics proved to be a promising metric for tracking fracture healing in individual patients.
