Nguyễn Mỹ-Vân, Carlier Christophe, Nich Christophe, Gouin François, Crenn Vincent
Orthopedics and Trauma Department, University Hospital Hôtel-Dieu, UHC of Nantes, 44000 Nantes, France.
PhyOs 1238, INSERM, University of Nantes, 44000 Nantes, France.
Cancers (Basel). 2021 Jul 21;13(15):3662. doi: 10.3390/cancers13153662.
Long bone pathological fractures very much reflect bone metastases morbidity in many types of cancer. Bearing in mind that they not only compromise patient function but also survival, identifying impending fractures before the actual event is one of the main concerns for tumor boards. Indeed, timely prophylactic surgery has been demonstrated to increase patient quality of life as well as survival. However, early surgery for long bone metastases remains controversial as the current fracture risk assessment tools lack accuracy. This review first focuses on the gold standard Mirels rating system. It then explores other unique imaging thresholds such as axial or circumferential cortical involvement and the merits of nuclear imaging tools. To overcome the lack of specificity, other fracture prediction strategies have focused on biomechanical models based on quantitative computed tomography (CT): computed tomography rigidity analysis (CT-RA) and finite element analysis (CT-FEA). Despite their higher specificities in impending fracture assessment, their limited availability, along with a need for standardization, have limited their use in everyday practice. Currently, the prediction of long bone pathologic fractures is a multifactorial process. In this regard, machine learning could potentially be of value by taking into account clinical survival prediction as well as clinical and improved CT-RA/FEA data.
长骨病理性骨折在很大程度上反映了多种癌症中的骨转移发病率。鉴于长骨病理性骨折不仅会损害患者的功能,还会影响其生存,在实际骨折发生前识别即将发生的骨折是肿瘤委员会的主要关注点之一。事实上,及时的预防性手术已被证明可以提高患者的生活质量和生存率。然而,由于目前的骨折风险评估工具缺乏准确性,长骨转移瘤的早期手术仍存在争议。本综述首先聚焦于金标准米雷尔斯评分系统。然后探讨其他独特的影像学阈值,如轴向或周向皮质受累情况以及核成像工具的优点。为了克服特异性不足的问题,其他骨折预测策略聚焦于基于定量计算机断层扫描(CT)的生物力学模型:计算机断层扫描刚度分析(CT-RA)和有限元分析(CT-FEA)。尽管它们在预测即将发生的骨折方面具有更高的特异性,但可用性有限以及需要标准化,限制了它们在日常实践中的应用。目前,长骨病理性骨折的预测是一个多因素过程。在这方面,机器学习通过考虑临床生存预测以及临床和改进的CT-RA/FEA数据可能具有潜在价值。