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基于统计多参数建模的髋部骨折鉴别。

Hip Fracture Discrimination Based on Statistical Multi-parametric Modeling (SMPM).

机构信息

Department of Radiology, University of Colorado Anschutz Medical Campus, 12700 E 19th Ave, Room 1208, Mail Stop C278, Aurora, CO, 80045, USA.

Department of Radiology, Beijing Jishuitan Hospital, Beijing, China.

出版信息

Ann Biomed Eng. 2019 Nov;47(11):2199-2212. doi: 10.1007/s10439-019-02298-x. Epub 2019 May 31.

Abstract

Studies using quantitative computed tomography (QCT) and data-driven image analysis techniques have shown that trabecular and cortical volumetric bone mineral density (vBMD) can improve the hip fracture prediction of dual-energy X-ray absorptiometry areal BMD (aBMD). Here, we hypothesize that (1) QCT imaging features of shape, density and structure derived from data-driven image analysis techniques can improve the hip fracture discrimination of classification models based on mean femoral neck aBMD (Neck.aBMD), and (2) that data-driven cortical bone thickness (Ct.Th) features can improve the hip fracture discrimination of vBMD models. We tested our hypotheses using statistical multi-parametric modeling (SMPM) in a QCT study of acute hip fracture of 50 controls and 93 fragility fracture cases. SMPM was used to extract features of shape, vBMD, Ct.Th, cortical vBMD, and vBMD in a layer adjacent to the endosteal surface to develop hip fracture classification models with machine learning logistic LASSO. The performance of these classification models was evaluated in two aspects: (1) their hip fracture classification capability without Neck.aBMD, and (2) their capability to improve the hip fracture classification of the Neck.aBMD model. Assessments were done with 10-fold cross-validation, areas under the receiver operating characteristic curve (AUCs), differences of AUCs, and the integrated discrimination improvement (IDI) index. All LASSO models including SMPM-vBMD features, and the majority of models including SMPM-Ct.Th features performed significantly better than the Neck.aBMD model; and all SMPM features significantly improved the hip fracture discrimination of the Neck.aBMD model (Hypothesis 1). An interesting finding was that SMPM-features of vBMD also captured Ct.Th patterns, potentially explaining the superior classification performance of models based on SMPM-vBMD features (Hypothesis 2). Age, height and weight had a small impact on model performances, and the model of shape, vBMD and Ct.Th consistently yielded better performances than the Neck.aBMD models. Results of this study clearly support the relevance of bone density and quality on the assessment of hip fracture, and demonstrate their potential on patient and healthcare cost benefits.

摘要

研究表明,基于定量计算机断层扫描(QCT)和数据驱动的图像分析技术,对小梁和皮质体积骨矿物质密度(vBMD)进行研究,可以提高双能 X 射线吸收法(DXA)面积骨矿物质密度(aBMD)对髋部骨折的预测能力。在这里,我们假设:(1)数据驱动的图像分析技术所得到的形状、密度和结构的 QCT 成像特征,可以提高基于股骨颈平均 aBMD(Neck.aBMD)的分类模型对髋部骨折的区分能力;(2)数据驱动的皮质骨厚度(Ct.Th)特征可以提高 vBMD 模型对髋部骨折的区分能力。我们在一项针对 50 名对照者和 93 例脆性骨折患者的急性髋部骨折 QCT 研究中,使用统计多参数建模(SMPM)来检验我们的假设。使用 SMPM 从形状、vBMD、Ct.Th、皮质 vBMD 和靠近骨内膜表面的一层的 vBMD 中提取特征,利用机器学习逻辑 LASSO 开发髋部骨折分类模型。使用 10 折交叉验证、受试者工作特征曲线(ROC)下面积(AUCs)、AUC 差异和综合判别改善(IDI)指数评估这些分类模型的性能:(1)没有 Neck.aBMD 时,这些分类模型对髋部骨折的分类能力;(2)这些分类模型对 Neck.aBMD 模型髋部骨折分类能力的改善能力。所有包含 SMPM-vBMD 特征的 LASSO 模型,以及大多数包含 SMPM-Ct.Th 特征的模型,其表现均明显优于 Neck.aBMD 模型;所有 SMPM 特征均显著提高了 Neck.aBMD 模型对髋部骨折的区分能力(假设 1)。一个有趣的发现是,SMPM-vBMD 特征还捕捉到了 Ct.Th 模式,这可能解释了基于 SMPM-vBMD 特征的模型具有更好分类性能的原因(假设 2)。年龄、身高和体重对模型性能的影响较小,而形状、vBMD 和 Ct.Th 的模型表现始终优于 Neck.aBMD 模型。本研究结果清楚地支持了骨密度和质量在髋部骨折评估中的相关性,并证明了它们在患者和医疗保健成本效益方面的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7cf6/7012369/facbb06bd5f3/nihms-1553193-f0001.jpg

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