Aldieri Alessandra, Terzini Mara, Audenino Alberto L, Bignardi Cristina, Morbiducci Umberto
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy; PolitoBIOMed Lab, Politecnico di Torino, Turin, Italy.
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy; PolitoBIOMed Lab, Politecnico di Torino, Turin, Italy.
Comput Biol Med. 2020 Dec;127:104093. doi: 10.1016/j.compbiomed.2020.104093. Epub 2020 Oct 27.
Aiming to improve osteoporotic hip fracture risk detection, factors other than the largely adopted Bone Mineral Density (BMD) have been investigated as potential risk predictors. In particular Hip Structural Analysis (HSA)-derived parameters accounting for femur geometry, extracted from Dual-energy X-ray Absorptiometry (DXA) images, have been largely considered as geometric risk factors. However, HSA-derived parameters represent discrete and cross-correlated quantities, unable to describe proximal femur geometry as a whole and tightly related to BMD. Focusing on a post-menopausal cohort (N = 28), in this study statistical models of bone shape and BMD distribution have been developed to investigate their possible role in fracture risk. Due to unavailable retrospective patient-specific fracture risk information, here a surrogate fracture risk based on 3D computer simulations has been employed for the statistical framework construction. When considered separately, BMD distribution performed better than shape in explaining the surrogate fracture risk variability for the analysed cohort. However, the combination of BMD and femur shape quantities in a unique statistical model yielded better results. In detail, the first shape-intensity combined mode identified using a Partial Least Square (PLS) algorithm was able to explain 70% of the surrogate fracture risk variability, thus suggesting that a more effective patients stratification can be obtained applying a shape-intensity combination approach, compared to T-score. The findings of this study strongly advocate future research on the role of a combined shape-BMD statistical framework in fracture risk determination.
为了提高骨质疏松性髋部骨折风险检测水平，除了广泛采用的骨密度（BMD）之外，其他因素也被作为潜在风险预测指标进行了研究。特别是，从双能X线吸收法（DXA）图像中提取的、考虑股骨几何形状的髋部结构分析（HSA）衍生参数，在很大程度上被视为几何风险因素。然而，HSA衍生参数代表离散且相互关联的量，无法整体描述股骨近端几何形状，且与骨密度密切相关。本研究聚焦于绝经后队列（N = 28），建立了骨形状和骨密度分布的统计模型，以研究它们在骨折风险中的可能作用。由于缺乏回顾性的患者特异性骨折风险信息，这里采用了基于三维计算机模拟的替代骨折风险来构建统计框架。单独考虑时，骨密度分布在解释分析队列的替代骨折风险变异性方面比形状表现更好。然而，在一个独特的统计模型中将骨密度和股骨形状量结合起来能产生更好的结果。具体而言，使用偏最小二乘（PLS）算法识别出的第一个形状 - 强度组合模式能够解释70%的替代骨折风险变异性，这表明与T值相比，应用形状 - 强度组合方法可以获得更有效的患者分层。本研究结果强烈支持未来对形状 - 骨密度联合统计框架在骨折风险判定中的作用进行研究。
