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

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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.

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Acknowledgments

This work was supported by the NIH/NIAMS under grants R01AR068456 and R01AR064140. This study was also supported by grants from the National Natural Science Foundation of China (81071131), the Beijing Bureau of Health 215 Program (2013-3-033; 2009-2-03), Beijing Technology Foundation for Selected Overseas Chinese Scholar and Beijing Talents Fund (2015000021467), Capital Characteristic Clinic Project (Z141107002514072).

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Authors and Affiliations

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

    Julio Carballido-Gamio

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

    Aihong Yu, Ling Wang, Yongbin Su & Xiaoguang Cheng

  3. Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA

    Andrew J. Burghardt & Thomas F. Lang

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  1. Julio Carballido-Gamio
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Correspondence to Julio Carballido-Gamio.

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Appendix

Appendix

This section has the purpose of supporting the necessity of a third PCA step to address potential correlations between shape and feature principal component scores.

In this study, for the first 10 principal components, out of 100 correlations of principal component scores: (1) 37% between shape and vBMD, (2) 43% between shape and Ct.Th, (3) 29% between shape and Ct.vBMD, and (4) 40% between shape and EndoTb.vBMD, were significant.

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Carballido-Gamio, J., Yu, A., Wang, L. et al. Hip Fracture Discrimination Based on Statistical Multi-parametric Modeling (SMPM). Ann Biomed Eng 47, 2199–2212 (2019). https://doi.org/10.1007/s10439-019-02298-x

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