Bone texture analysis of human femurs using a new device (BMA™) improves failure load prediction.

UNLABELLED: We measured bone texture parameters of excised human femurs with a new device (BMA™). We also measured bone mineral density by DXA and investigated the performance of these parameters in the prediction of failure load. Our results suggest that bone texture parameters improve failure load prediction when added to bone mineral density.

INTRODUCTION: Bone mineral density (BMD) is a strong determinant of bone strength. However, nearly half of the fractures occur in patients with BMD which does not reach the osteoporotic threshold. In order to assess fracture risk properly, other factors are important to be taken into account such as clinical risk factors as well as macro- and microarchitecture of bone. Bone microarchitecture is usually assessed by high-resolution QCT, but this cannot be applied in routine clinical settings due to irradiation, cost and availability concerns. Texture analysis of bone has shown to be correlated to bone strength.

METHODS: We used a new device to get digitized X-rays of 12 excised human femurs in order to measure bone texture parameters in three different regions of interest (ROIs). We investigated the performance of these parameters in the prediction of the failure load using biomechanical tests. Texture parameters measured were the fractal dimension (Hmean), the co-occurrence matrix, and the run length matrix. We also measured bone mineral density by DXA in the same ROIs as well as in standard DXA hip regions.

RESULTS: The Spearman correlation coefficient between BMD and texture parameters measured in the same ROIs ranged from -0.05 (nonsignificant (NS)) to 0.57 (p = 0.003). There was no correlation between Hmean and co-occurrence matrix nor Hmean and run length matrix in the same ROI (r = -0.04 to 0.52, NS). Co-occurrence matrix and run length matrix in the same ROI were highly correlated (r = 0.90 to 0.99, p < 0.0001). Univariate analysis with the failure load revealed significant correlation only with BMD results, not texture parameters. Multiple regression analysis showed that the best predictors of failure load were BMD, Hmean, and run length matrix at the femoral neck, as well as age and sex, with an adjusted r (2) = 0.88. Added to femoral neck BMD, Hmean and run length matrix at the femoral neck (without the effect of age and sex) improved failure load prediction (compared to femoral neck BMD alone) from adjusted r (2) = 0.67 to adjusted r (2) = 0.84.

CONCLUSION: Our results suggest that bone texture measurement improves failure load prediction when added to BMD.