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.

ObjectivesIn 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.MethodsA 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.ResultsIn institute 1, median failure load was significantly lower for patients who sustained a fracture than for patients with no fractures. In institutes 2 and 3, the number of patients with a fracture was too low to make a clear distinction. Fracture locations were well predicted by the FE model when compared with post-fracture radiographs. The FE model was more accurate in identifying patients with a high fracture risk compared with experienced clinicians, with a sensitivity of 89% versus 0% to 33% for clinical assessments. Specificity was 79% for the FE models versus 84% to 95% for clinical assessments.ConclusionFE models can be a valuable tool to improve clinical fracture risk predictions in metastatic bone disease. Future work in a larger patient population should confirm the higher predictive power of FE models compared with current clinical guidelines.Cite this article: F. Eggermont, L. C. Derikx, N. Verdonschot, I. C. M. van der Geest, M. A. A. de Jong, A. Snyers, Y. M. van der Linden, E. Tanck. Can patient-specific finite element models better predict fractures in metastatic bone disease than experienced clinicians? Towards computational modelling in daily clinical practice. Bone Joint Res 2018;7:430–439. DOI: 10.1302/2046-3758.76.BJR-2017-0325.R2.

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 (R(2) = 0.90 and R(2) = 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.

To aid in therapy selection for patients with spinal bone metastases (SBM), predictive models have been developed. These models consider SBM from breast cancer a positive predictive factor, but do not take phenotypes based on estrogen (ER), progesterone (PR) and human epidermal growth factor 2 (HER2) receptors into account. The aim of this study was to ascertain whether receptors are associated with survival, when the disease has progressed up to SBM. All patients who were treated for SBM from breast cancer between 2005 and 2012 were included in this international multi-center retrospective study (n = 111). Reports were reviewed for ER, PR and HER2 status and subsequently subdivided into one of four categories; luminal A, luminal B, HER2 and triple negative. Survival time was calculated as the difference between start of treatment for SBM and date of death. Analysis was performed using the Kaplan-Meier method and log-rank tests. Median follow-up was 3.7 years. Survival times in the luminal B and HER2 categories were not significantly different to the luminal A category and were joined into a single receptor positive category. Eighty-five patients (77 %) had a receptor positive phenotype and 25 (23 %) had a triple negative phenotype. Median survival time was 22.5 months (95 %CI 18.0-26.9) for the receptor positive category and 6.7 months (95 %CI 2.4-10.9) for the triple negative category (p < 0.001). Patients with SBM from breast cancer with a triple negative phenotype have a shorter survival time than patients with a receptor positive phenotype. Models estimating survival should be adjusted accordingly.

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