PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Karl D Fritscher; Marta Peroni; Paolo Zaffino; Maria Francesca Spadea; Rainer Schubert; Gregory Sharp Journal: Med Phys Date: 2014-05 Impact factor: 4.071
Authors: Julio Carballido-Gamio; Aihong Yu; Ling Wang; Yongbin Su; Andrew J Burghardt; Thomas F Lang; Xiaoguang Cheng Journal: Ann Biomed Eng Date: 2019-05-31 Impact factor: 3.934
Authors: Julio Carballido-Gamio; Roy Harnish; Isra Saeed; Timothy Streeper; Sigurdur Sigurdsson; Shreyasee Amin; Elizabeth J Atkinson; Terry M Therneau; Kristin Siggeirsdottir; Xiaoguang Cheng; L Joseph Melton; Joyce H Keyak; Vilmundur Gudnason; Sundeep Khosla; Tamara B Harris; Thomas F Lang Journal: Bone Date: 2013-08-25 Impact factor: 4.398
Authors: Isaac Castro-Mateos; Jose M Pozo; Timothy F Cootes; J Mark Wilkinson; Richard Eastell; Alejandro F Frangi Journal: Curr Osteoporos Rep Date: 2014-06 Impact factor: 5.096