Metastatic Spine Tumors: Minimally Invasive Fracture Risk Analysis and Treatment - Master

项目摘要/摘要 癌症转移到骨性脊柱的识别使治疗的临床医生有义务做出 手术决定。目前的脊柱稳定性决策是经验性的、定性的，可能是不准确的。这个 然而，这一决定对患者的影响是重大的。如果脊椎被认为有可能 骨折，然后病人将接受一个重大的脊柱手术。相反，脊椎被认为 如果分析不正确，稳定的风险就会破裂，可能会瘫痪。这项研究计划解决了这两个问题 稳定性的决定和治疗的性质。在这次续期申请中，我们将继续努力 发展无创、定量和可靠的方法来预测转移性椎体骨折的风险 在生理相关负荷条件下的肿瘤，并优化使用 新的生物材料，以重建受影响脊椎的承载能力。在目标1中，我们提出了一个 新型可注入聚合物网络，可通过无催化剂点击化学自交联成“点击” 有机-无机纳米杂化(点击)骨水泥。与我们之前的注射系统相比，新的 水泥改善了生物相容性、可注入性和交联率。在目标2中，我们将调查 优化的点击式骨水泥预防和治疗即将发生的骨折的疗效 在身体模型中，分别使用临床椎体成形术和后凸成形术(目标2a)。完好无损 腰椎(L1-S1)、具有模拟溶解缺陷的脊柱以及使用生物材料增强的脊柱将被 使用一种新型的机器人测试系统，在准确的仿生加载条件下进行测试。我们以前的 开发的基于计算机断层扫描的定量有限元分析(QCT/FEA)模型将 扩展到包括生理负荷下的运动学运动评估和骨折风险预测 和边界条件，并用实验结果进行了验证(目标2b)。在目标3中，我们将制定一个 无模校准技术，以考虑QCT方案对QCT/FEA结果的影响(目标3a)。 使用强大的AnalyzeMD平台，我们将实施自动化流程，以进一步推进有限元分析 提高时间效率和再现性的技术(目标3b)。我们将应用全面的QCT/FEA模型 在脊柱转移患者的回顾队列中，并使用点击来评估虚拟重建 骨水泥作为临床移植的第一步。在这项工作中开发的QCT/FEA技术 同时考虑了骨骼的质量和数量以及椎间盘的退变状况。 这项技术允许临床医生就日常生活活动向她/他的患者提供咨询， 手术的脊柱骨折风险很低。我们未来的计划是扩大临床应用的范围 梅奥诊所的脊柱有限元分析。我们将在与患者的讨论中加入有限元评估结果 关于我们对他们的护理建议。我们将研究这些建议的结果， 根据需要调整决策参数，然后将分析扩展到更多的机构。

Project Summary/Abstract The identification of cancer metastases to the bony vertebral column obligates the treating clinician to make a surgical decision. Current spinal stability decision-making is empirical, qualitative, and can be inaccurate. The consequences of that decision for the patient, however, are significant. If the spine is deemed at risk for fracture, then the patient will undergo a major spinal operation. Conversely, the patient whose spine is deemed stable risks fracture and possible paralysis if the analysis was incorrect. This research program addresses both the stability decision and the nature of the treatment. In this renewal application, we will continue our efforts to develop non-invasive, quantitative, and reliable methods to predict the fracture risk of vertebrae with metastatic cancer under physiologically relevant loading conditions, and to optimize minimally invasive techniques using novel biomaterials to reconstitute the load bearing capacity of an affected vertebra. In Aim 1, we propose a novel injectable polymer network that can be self-crosslinked via catalyst-free click chemistry into “click” organic-inorganic nanohybrid (click-ON) bone cement. Compared to our previous injectable system, the novel cement has improved biocompatibility, injectability, and crosslinking efficiency. In Aim 2, we will investigate the efficacy of the optimized click-ON bone cement to both prevent impending fractures and treat existing fractures in cadaveric models using the clinical vertebroplasty and kyphoplasty procedures, respectively (Aim 2a). Intact lumbar spines (L1-S1), spines with simulated lytic defects, and spines with biomaterial augmentation will be tested under accurate and biomimetic loading conditions using a novel robotic testing system. Our previously developed quantitative computerized tomography based finite element analysis (QCT/FEA) models will be expanded to include both kinematic motion evaluation and fracture risk prediction under physiological loading and boundary conditions and validated using the experimental results (Aim 2b). In Aim 3, We will develop a phantom-less calibration technique to account for the effects of QCT protocols on QCT/FEA results (Aim 3a). Using the powerful AnalyzeMD platform, we will implement an automated process to further advance the FEA technique for time efficiency and reproducibility (Aim 3b). We will apply the comprehensive QCT/FEA models in a retrospective cohort of spine metastasis patients and assess the virtual reconstruction using the click-ON bone cement as a first step towards clinical translation. The QCT/FEA technique developed in this work takes into consideration both the quality and quantity of bone and the degeneration status of the intervertebral discs. This technique allows the clinician to counsel her/his patient regarding activities of daily living that can be performed with a low risk of spinal fracture. Our future plans are to expand the clinical implementation of the spinal FEA analysis at Mayo Clinic. We will add FEA evaluation results in our discussion with the patients regarding our recommendations for their care. We will study the outcome results of those recommendations, adjust the decision parameters as necessary, and then extend the analysis to additional institutions.