In this prospective cohort study, we investigated whether patient-specific finite element (FE) models can identify patients at risk of a pathological femoral fracture resulting from metastatic bone disease, and compared these FE predictions with clinical assessments by experienced clinicians. A total of 39 patients with non-fractured femoral metastatic lesions who were irradiated for pain were included from three radiotherapy institutes. During follow-up, nine pathological fractures occurred in seven patients. Quantitative CT-based FE models were generated for all patients. Femoral failure load was calculated and compared between the fractured and non-fractured femurs. Due to inter-scanner differences, patients were analyzed separately for the three institutes. In addition, the FE-based predictions were compared with fracture risk assessments by experienced clinicians.Objectives
Methods
Previously, we showed that case-specific non-linear
finite element (FE) models are better at predicting the load to failure
of metastatic femora than experienced clinicians. In this study
we improved our FE modelling and increased the number of femora
and characteristics of the lesions. We retested the robustness of
the FE predictions and assessed why clinicians have difficulty in
estimating the load to failure of metastatic femora. A total of
20 femora with and without artificial metastases were mechanically
loaded until failure. These experiments were simulated using case-specific
FE models. Six clinicians ranked the femora on load to failure and
reported their ranking strategies. The experimental load to failure
for intact and metastatic femora was well predicted by the FE models (R2 =
0.90 and R2 = 0.93, respectively). Ranking metastatic
femora on load to failure was well performed by the FE models (τ =
0.87), but not by the clinicians (0.11 <
τ <
0.42). Both the
FE models and the clinicians allowed for the characteristics of
the lesions, but only the FE models incorporated the initial bone
strength, which is essential for accurately predicting the risk
of fracture. Accurate prediction of the risk of fracture should
be made possible for clinicians by further developing FE models.