Accurate estimations of the risk of fracture due to metastatic bone disease in the femur is essential in order to avoid both under-treatment and over-treatment of patients with an impending pathological fracture. The purpose of the current retrospective in vivo study was to use CT-based finite element analyses (CTFEA) to identify a clear quantitative differentiating factor between patients who are at imminent risk of fracturing their femur and those who are not, and to identify the exact location of maximal weakness where the fracture is most likely to occur. Data were collected on 82 patients with femoral metastatic bone disease, 41 of whom did not undergo prophylactic fixation. A total of 15 had a pathological fracture within six months following the CT scan, and 26 were fracture-free during the five months following the scan. The Mirels score and strain fold ratio (SFR) based on CTFEA was computed for all patients. A SFR value of 1.48 was used as the threshold for a pathological fracture. The sensitivity, specificity, positive, and negative predicted values for Mirels score and SFR predictions were computed for nine patients who fractured and 24 who did not, as well as a comparison of areas under the receiver operating characteristic curves (AUC of the ROC curves).Aims
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.
