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
This study aims to assess first, whether mutations in the epidermal
growth factor receptor (EGFR) and Kirsten rat sarcoma (kRAS) genes
are associated with overall survival (OS) in patients who present
with symptomatic bone metastases from non-small cell lung cancer
(NSCLC) and secondly, whether mutation status should be incorporated into
prognostic models that are used when deciding on the appropriate
palliative treatment for symptomatic bone metastases. We studied 139 patients with NSCLC treated between 2007 and 2014
for symptomatic bone metastases and whose mutation status was known.
The association between mutation status and overall survival was
analysed and the results applied to a recently published prognostic
model to determine whether including the mutation status would improve
its discriminatory power.Aims
Patients and 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.
A number of risk factors based upon mostly retrospective surgical data, have been formulated in order to identify impending pathological fractures of the femur from low-risk metastases. We have followed up patients taking part in a randomised trial of radiotherapy, prospectively, in order to determine if these factors were effective in predicting fractures. In 102 patients with 110 femoral lesions, 14 fractures occurred during follow-up. The risk factors studied were increasing pain, the size of the lesion, radiographic appearance, localisation, transverse/axial/circumferential involvement of the cortex and the scoring system of Mirels. Only axial cortical involvement >
30 mm (p = 0.01), and circumferential cortical involvement >
50% (p = 0.03) were predictive of fracture. Mirels’ scoring system was insufficiently specific to predict a fracture (p = 0.36). Our results indicate that most conventional risk factors overestimate the actual occurrence of pathological fractures of the femur. The risk factor of axial cortical involvement provides a simple, objective tool in order to decide which treatment is appropriate.
