Characterization of the Mechanical Impact of Focal and Systemic Cancer Treatments on Healthy and Metastatically Involved Vertebral Bone
Abstract
Approximately one third of cancer patients are diagnosed with vertebral metastasis and associated bone quality impairment. Treatments for metastatic disease have proven beneficial to overall prognosis, yet their effects on bone quality are not fully characterized. This research utilizes image analysis, physical testing, and generative deep learning (DL) approaches to characterize bone quality and treatment-associated changes in a preclinical rat model of osteolytic vertebral metastasis. The specific aims are to enhance barium sulfate (BaSO4) labelled microdamage visualization in μCT images of vertebrae, determine tissue level material and mechanical properties of healthy and osteolytic vertebrae with and without treatment, and develop generative DL models predicting vertebral fracture. μCT image acquisition parameters were iteratively adjusted to enhance BaSO4 contrast and spatial resolution. Enhanced μCT image acquisition parameters yielded BaSO4 labels with high probability of pixelwise spatial correlation (83% within 20 μm) to microdamage evaluated in gold-standard backscatter electron (BSE) images. This work provides a high-resolution protocol for 3D μCT analysis of microdamage accumulation in vertebral bone. Microdamage accumulation, load-to-failure, and microstructural parameters were measured in healthy and osteolytic rat vertebrae with and without treatment (stereotactic body radiotherapy (SBRT), docetaxel, zoledronic acid). Microdamage was increased in metastatic…
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