Institute for Biomedical Image Analysis, University of Medical Informatics, Health Science and Technology (UMIT), 6060 Hall in Tirol, Austria.
Med Phys. 2010 Jun;37(6):2560-71. doi: 10.1118/1.3425791.
Standard diagnostic techniques to quantify bone mineral density (BMD) include dual-energy x-ray absorptiometry (DXA) and quantitative computed tomography. However, BMD alone is not sufficient to predict the fracture risk for an individual patient. Therefore, the development of tools, which can assess the bone quality in order to predict individual biomechanics of a bone, would mean a significant improvement for the prevention of fragility fractures. In this study, a new approach to predict the fracture risk of proximal femora using a statistical appearance model will be presented.
100 CT data sets of human femur cadaver specimens are used to create statistical appearance models for the prediction of the individual fracture load (FL). Calculating these models offers the possibility to use information about the inner structure of the proximal femur, as well as geometric properties of the femoral bone for FL prediction. By applying principal component analysis, statistical models have been calculated in different regions of interest. For each of these models, the individual model parameters for each single data set were calculated and used as predictor variables in a multilinear regression model. By this means, the best working region of interest for the prediction of FL was identified. The accuracy of the FL prediction was evaluated by using a leave-one-out cross validation scheme. Performance of DXA in predicting FL was used as a standard of comparison.
The results of the evaluative tests demonstrate that significantly better results for FL prediction can be achieved by using the proposed model-based approach (R = 0.91) than using DXA-BMD (R = 0.81) for the prediction of fracture load.
The results of the evaluation show that the presented model-based approach is very promising and also comparable to studies that partly used higher image resolutions for bone quality assessment and fracture risk prediction.
定量骨密度（BMD）的标准诊断技术包括双能 X 射线吸收法（DXA）和定量计算机断层扫描。然而，仅 BMD 不足以预测个体患者的骨折风险。因此，开发能够评估骨质量以预测个体骨骼生物力学的工具，将意味着对预防脆性骨折的重大改进。在这项研究中，提出了一种使用统计外观模型预测股骨近端骨折风险的新方法。
使用 100 个人体股骨尸体标本的 CT 数据集创建用于预测个体骨折负荷（FL）的统计外观模型。计算这些模型提供了使用股骨近端内部结构以及股骨几何特性的信息来预测 FL 的可能性。通过应用主成分分析，在不同的感兴趣区域计算了统计模型。对于每个模型，计算了每个单独数据集的单个模型参数，并将其用作多元线性回归模型中的预测变量。通过这种方式，确定了用于预测 FL 的最佳工作感兴趣区域。通过使用留一交叉验证方案评估 FL 预测的准确性。DXA 预测 FL 的性能用作比较标准。
评估测试的结果表明，与使用 DXA-BMD（R=0.81）相比，使用基于模型的拟议方法（R=0.91）可以实现更好的 FL 预测结果。
评估结果表明，所提出的基于模型的方法非常有前途，并且与部分使用更高图像分辨率进行骨质量评估和骨折风险预测的研究相当。
