Patients with cancer and bone metastases can have an increased risk of fracturing their femur. Treatment is based on the impending fracture risk: patients with a high fracture risk are considered for prophylactic surgery, whereas low fracture risk patients are treated conservatively with radiotherapy to decrease pain. Current clinical guidelines suggest to determine fracture risk based on axial cortical involvement of the lesion on conventional radiographs, but that appears to be difficult. Therefore, we developed a patient-specific finite element (FE) computer model that has shown to be able to predict fracture risk in an experimental setting and in patients. The goal of this study was to determine whether patient-specific finite element (FE) computer models are better at predicting fracture risk for femoral bone metastases compared to clinical assessments based on axial cortical involvement on conventional radiographs, as described in current clinical guidelines.

45 patients (50 affected femurs) affected with predominantly lytic bone metastases who were treated with palliative radiotherapy for pain were included. CT scans were made and patients were followed for six months to determine whether or not they fractured their femur. Non-linear isotropic FE models were created with the patient-specific geometry and bone density obtained from the CT scans. Subsequently, an axial load was simulated on the models mimicking stance. Failure loads normalized for bodyweight (BW) were calculated for each femur. High and low fracture risks were determined using a failure load of 7.5 × BW as a threshold. Experienced assessors measured axial cortical involvement on conventional radiographs. Following clinical guidelines, patients with lesions larger than 30 mm were identified as having a high fracture risk. FE predictions were compared to clinical assessments by means of diagnostic accuracy values (sensitivity, specificity and positive (PPV) and negative predictive values (NPV)).

Seven femurs (14%) fractured during follow-up. Median time to fracture was 8 weeks. FE models were better at predicting fracture risk in comparison to clinical assessments based on axial cortical involvement (sensitivity 100% vs. 86%, specificity 74% vs. 42%, PPV 39% vs. 19%, and NPV 100% vs. 95%, for the FE computer model vs. axial cortical involvement, respectively). We concluded that patient-specific FE computer models improve fracture risk predictions of femoral bone metastases in advanced cancer patients compared to clinical assessments based on axial cortical involvement, which is currently used in clinical guidelines. Therefore, we are initiating a pilot for clinical implementation of the FE model.

Patients with advanced cancer can develop bone metastases in the femur which are often painful and increase the risk of pathological fracture. Accurate segmentation of bone metastases is, amongst others, important to improve patient-specific computer models which calculate fracture risk, and for radiotherapy planning to determine exact radiation fields. Deep learning algorithms have shown to be promising to improve segmentation accuracy for metastatic lesions, but require reliable segmentations as training input. The aim of this study was to investigate the inter- and intra-operator reliability of manual segmentation of femoral metastatic lesions and to define a set of lesions which can serve as a training dataset for deep learning algorithms. F

CT-scans of 60 advanced cancer patients with a femur affected with bone metastases (20 osteolytic, 20 osteoblastic and 20 mixed) were used in this study. Two operators were trained by an experienced radiologist and then segmented the metastatic lesions in all femurs twice with a four-week time interval. 3D and 2D Dice coefficients (DCs) were calculated to quantify the inter- and intra-operator reliability of the segmentations. We defined a DC>0.7 as good reliability, in line with a statistical image segmentation study.

Mean first and second inter-operator 3D-DCs were 0.54 (±0.28) and 0.50 (±0.32), respectively. Mean intra-operator I and II 3D-DCs were 0.56 (±0.28) and 0.71 (±0.23), respectively. Larger lesions (>60 cm³) scored higher DCs in comparison with smaller lesions.

This study reveals that manual segmentation of metastatic lesions is challenging and that the current manual segmentation approach resulted in dissatisfying outcomes, particularly for lesions with small volumes. However, segmentation of larger lesions resulted in a good inter- and intra-operator reliability. In addition, we were able to select 521 slices with good segmentation reliability that can be used to create a training dataset for deep learning algorithms. By using deep learning algorithms, we aim for more accurate automated lesion segmentations which might be used in computer modelling and radiotherapy planning.

Objectives

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.

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.

Bone metastases occur in about 15% of all cancer cases. Pathological fractures that result from these tumours most frequently occur in the femur. It is extremely difficult to determine the fracture risk with the current X-ray methods, even for experienced physicians. The purpose of this study was to assess whether the use of a predictive finite element model could improve the prediction of strength in comparison to an clinical assessment.

Eight human cadaver femora, with and without simulated metastases, were CT-scanned. A solid calibration phantom was included in each scan. From the scans, eight finite element (FE) models were generated using brick elements. The non-linear mechanical properties were based on bone density. After scanning, laboratory experiments were performed. The femora were loaded under compression until failure. During the experiments the failure forces and the course of failure were registered. These experiments were simulated in the FE-models, in which plastic deformation simulated failure of the bones. Six experienced physicians, were asked to rank the femora on strength using X-rays (AP and ML) and additional information on gender and age.

The results showed a strong Pearson’s correlation (r2 = 0.92) between the experimental failure force and predicted failure force. The Spearman’s rank correlations between experiment and predictions ranged between ρ=0.58 and ρ=0.8 for the physicians, whereas it was significantly higher (ρ=0.92) for the FE-model

This study showed that femur specific FE models better predicted femoral failure risk under axial loading than experienced physicians. When the model is further improved by adding, for example, other loading conditions, it can be clinically implemented to predict in vivo fracture risk for patients suffering, for example, bone metastases or osteoporosis.

Mechanical loading is important for the maintenance of the skeleton. In this study we addressed the following question. What is the influence of long-term exposure to 2.5 g on bone architecture in male rats? We expect that bone density will increase.

For the experiments we used a total of 14 Long Evans rats. Two experiments were performed in which the rats were exposed to 2.5 g for a period between 33 and 44 weeks. In the first experiment we analyzed the 3D trabecular structure in the femoral head, and in the second one the structure in the proximal tibia (metaphysis) was analyzed using micro-computer-tomography.

Rats exposed to 2.5 g had between 6% and 29% less total body weight than controls. Changes in anisotropy, which is a measure for trabecular alignment, were negligible. In the femoral head, the bone volume fraction (BV/TV) was similar for rats exposed to 2.5 g and controls. The diameters of the femoral head and neck in rats exposed to hypergravity were smaller than in controls, but not significantly. In the tibia, the BV/TV was lower for rats exposed to 2.5 g than for control rats (p< 0.05), whereas the size of the tibial plateau was larger in the exposed rats (p< 0.05).

These preliminary results were in contrast to our expectation. When exposed to 2.5 g, the trabecular architecture in the femoral head hardly changed, and in the tibia the BV/TV decreased. The tibial plateau was however larger. Adaptation to hypergravity conditions might be more at the global, cortical level than at the trabecular level. Alternatively, it is possible that the activity of rats exposed to hypergravity was less compared to controls. This would result in decreased dynamic stimulation of the bone so that the BV/TV still may satisfy the mechanical demands of rats exposed to hyper-gravity.