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Quantifying Acute Cortical Bone Changes Under Clinical Constraints

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Quantifying Acute Cortical Bone Changes Under Clinical Constraints
Borg, Alexander Meo
Although radiation therapy (RT) is quintessential to oncology, it poses significant complications which often inhibit a robust recovery. Structural and material skeletal deterioration increases catastrophic fracture and osteoradionecrosis (ORN) incidences. Bisphosphonate countermeasures are routinely prescribed concurrently to patients identified as most at risk of serious sequelae. However, contemporary predictive models struggle to characterize the relationship between acute bone changes and chronic pathologies, obscuring which patients would benefit most from bisphosphonate intervention. RT-induced bone damage appears to occur proportionally to locally absorbed dose, though limitations in quantifying in vivo changes have long obscured its etiology, prediction, and prevention.Our group has previously developed a workflow which characterizes spatiotemporal cortical bone changes. However, the process estimates these changes as a function of RT dose, and requires extensive user training and surveillance. The primary goal of this endeavor is to develop software to calculate RT-derived bone changes automatically and without recondite expertise or constant oversight. The inputs to the software will remain the same: native RT prescription files and longitudinal computed tomography (CT) image sets. We hypothesize that exposure to radiation results in quantifiable changes to cortical bone thickness (C.Th), which can be calculated automatically using data already clinically available. Complications of RT, like ORN and fractures, are difficult to routinely characterize and predict due to limitations in clinical CT resolution. Our existing framework correlates spatial C.Th changes to RT dose by manually discretizing anatomies into homologous regions. By redefining this methodology, we now present novel software that automatically converts clinical RT prescriptions to mathematical matrices of dose points in a novel fashion. Anatomical discretization is no longer necessary, nor are several other steps in the process (e.g., manual landmark placing for registration). The process now requires minimal user oversight, thereby augmenting its repeatability and clinical viability. To validate the software, it was applied retrospectively to two populations having received RT to determine its effects upon mandibles and rib cages. The first population was treated with intensity-modulated RT or volumetric modulated arc therapy for head and neck cancers. The mandible in these patients is often irradiated due to its close proximity to the treatment site, rendering it a common source of unpredictable, RT-related sequelae like ORN. In N=49 patients, we observed acute, dose-dependent cortical bone thinning. Significantly more thinning occurred in regions having received over 20 Gy, with more thinning in more highly dosed regions. The second population was treated with stereotactic body radiation therapy (SBRT) for peripheral non-small cell lung cancer (NSCLC) lesions within 2 cm of the chest wall. Because the ribs of these patients are collaterally irradiated, this population has a heightened rate of undiagnosed rib fractures, increasing risk of serious consequences like myocardial contusions, ruptures, and pneumothorax. In this study, we retrospectively identified a cohort of NSCLC patients having received concurrent SBRT and bisphosphonate treatment, then matched it to a control population having never undergone bisphosphonate treatment (N=28 total). Bisphosphonate patients were found to have lost significantly less bone at regions prescribed under 20 Gy compared to the control population. Our development and deployment of automated software to investigate the acute effects of RT on C.Th represents a significant step in translating a previously esoteric process to the clinic. The results automatically output by the software agree with those in literature and those previously ascertained manually by our group. These findings suggest that a predictive clinical tool may be formulated to identify patients most at risk of RT-induced bone complications.
automatic big data
clinical pragmatism
cortical bone thickness
graphical user interface
medical imaging
radiation oncology
Willey, Jeffrey S (committee chair)
Danelson, Kerry A (committee member)
Farris, Michael K (committee member)
Hughes, Ryan T (committee member)
2022-05-24T08:35:40Z (accessioned)
2024-05-23T08:30:06Z (available)
2022 (issued)
Biomedical Engineering (discipline)
2024-05-23 (terms)
http://hdl.handle.net/10339/100714 (uri)
en (iso)
Wake Forest University

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