Pathological fracture prediction in patients with metastatic lesions can be improved with quantitative computed tomography based computer models
Tanck, Esther and Aken van, Jantien B. and Linden van der, Yvette M. and Schreuder, H.W. Bart and Binkowski, Marcin and Huizenga, Henk and Verdonschot, Nico (2009) Pathological fracture prediction in patients with metastatic lesions can be improved with quantitative computed tomography based computer models. Bone, 45 (4). pp. 777-783. ISSN 8756-3282
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|Abstract:||Purpose: In clinical practice, there is an urgent need to improve the prediction of fracture risk for cancer patients with bone metastases. The methods that are currently used to estimate fracture risk are dissatisfying, hence affecting the quality of life of patients with a limited life expectancy. The purpose of this study was to assess if non-linear finite element (FE) computer models, which are based on Quantitative Computer Tomography (QCT), are better than clinical experts in predicting bone strength.
Materials and methods: Ten human cadaver femurs were scanned using QCT. In one femur of each pair a hole (size 22, 40, or 45 mm diameter) was drilled at the anterior or medial side to simulate a metastatic lesion. All femurs were mechanically tested to failure under single-limb stance-type loading. The failure force was calculated using non-linear FE-models, and six clinical experts were asked to rank the femurs from weak to strong based on X-rays, gender, age, and the loading protocol. Kendall Tau correlation coefficients were calculated to compare the predictions of the FE-model with the predictions of the clinicians.
Results: The FE-failure predictions correlated strongly with the experimental failure force (r2 = 0.92, p < 0.001). For the clinical experts, the Kendall Tau coefficient between the experimental ranking and predicted ranking ranged between τ = 0.39 and τ = 0.72, whereas this coefficient was considerably higher (τ = 0.78) for the FE-model.
Conclusion: This study showed that the use of a non-linear FE-model can improve the prediction of bone strength compared to the prediction by clinical experts.
|Copyright:||© 2009 Elsevier|
Engineering Technology (CTW)
|Link to this item:||http://purl.utwente.nl/publications/80130|
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