背景:当影响股骨时,转移会增加骨折的风险。因此,临床医生需要知道患者的股骨是否能承受日常活动的压力。目前在诊所中使用的工具不够精确。一种新方法,基于CT扫描的有限元分析,给出了良好的预测结果。然而,现有模型均未进行可重复性测试.为了将该技术应用于世界各地的大型队列以帮助评估患者的骨转移性骨折风险,这是一个需要解决的关键问题。然后,这项研究的目的是评估1)可重复性2)复制模型到另一个数据集的转置和3)文献中最有前途的模型之一(原始模型)的全局敏感性。
方法:该模型是根据描述它的论文和与作者的讨论进行复制的,以避免复制错误。通过比较原始第一团队在原始模型中给出的结果来评估可重复性(Leuven,比利时)和另一个团队制作的复制模型(里昂,法国)在相同的离体股骨CT扫描数据集上。通过在两个不同数据集上比较再现模型的结果来评估模型的转置。利用Morris方法进行全局灵敏度分析,评价密度校正系数的影响,分割,股骨的方向和长度。
结果:原始模型和复制模型高度相关(r2=0.95),即使复制的模型给出了系统更高的失效载荷。在另一个数据集上使用复制模型时,预测的准确性较低(r2随着实验失效载荷的降低,错误增加)。全局灵敏度分析显示密度校准系数(84%的破坏载荷的平均变化)和不可忽略的影响分段的高影响,股骨的方向和长度(破坏载荷的平均变化在7%至10%之间)。
结论:这项研究表明,虽然正在验证,当使用另一个数据集时,复制的模型表现不佳。取决于数据集的性能差异通常是创建模型时过度拟合的原因。然而,原始论文中使用的数据集(Sas等人,,2020a)和鲁汶的数据集给出了类似的性能,这表明过度拟合原因的概率较小。此外,该模型对密度参数高度敏感,自动化测量可以最大限度地减少失效载荷的不确定性。不确定性传播分析将提供此类模型的实际精度,并提高我们对其行为的理解,并且是未来工作的一部分。
BACKGROUND: Metastases increase the risk of fracture when affecting the
femur. Consequently, clinicians need to know if the patient\'s
femur can withstand the stress of daily activities. The current tools used in clinics are not sufficiently precise. A new method, the CT-scan-based finite element analysis, gives good predictive results. However, none of the existing models were tested for reproducibility. This is a critical issue to address in order to apply the technique on a large cohort around the world to help evaluate bone metastatic fracture risk in patients. The aim of this study is then to evaluate 1) the reproducibility 2) the transposition of the reproduced model to another dataset and 3) the global sensitivity of one of the most promising models of the literature (original model).
METHODS: The model was reproduced based on the paper describing it and discussion with authors to avoid reproduction errors. The reproducibility was evaluated by comparing the results given in the original model by the original first team (Leuven, Belgium) and the reproduced model made by another team (Lyon, France) on the same dataset of CT-scans of ex vivo femurs. The transposition of the model was evaluated by comparing the results of the reproduced model on two different datasets. The global sensitivity analysis was done by using the Morris method and evaluates the influence of the density calibration coefficient, the segmentation, the orientations and the length of the
femur.
RESULTS: The original and reproduced models are highly correlated (r2 = 0.95), even though the reproduced model gives systematically higher failure loads. When using the reproduced model on another dataset, predictions are less accurate (r2 with the experimental failure load decreases, errors increase). The global sensitivity analysis showed high influence of the density calibration coefficient (mean variation of failure load of 84 %) and non-negligible influence of the segmentation, orientation and length of the
femur (mean variation of failure load between 7 and 10 %).
CONCLUSIONS: This study showed that, although being validated, the reproduced model underperformed when using another dataset. The difference in performance depending on the dataset is commonly the cause of overfitting when creating the model. However, the dataset used in the original paper (Sas et al., 2020a) and the Leuven\'s dataset gave similar performance, which indicates a lesser probability for the overfitting cause. Also, the model is highly sensitive to density parameters and automation of measurement may minimize the uncertainty on failure load. An uncertainty propagation analysis would give the actual precision of such model and improve our understanding of its behavior and is part of future work.