Fracture prediction in femoral bone metastases using the BOne Strength (BOS) Score

We developed the BOne Strength (BOS) Score, an easy-to-use tool to assess fracture risk of patients with femoral bone metastases. Patients with advanced cancer often develop bone metastases, and in approximately ten percent, these lesions occur in the femur. Femoral metastases may cause pain and can lead to pathological fractures, which severely affect the quality of life. Local treatment of patients with femoral metastases is based on the expected fracture risk: patients with a low fracture risk are treated conservatively with for example radiotherapy to decrease pain, whereas patients with a high fracture risk are considered for stabilizing surgery to prevent a fracture from occurring. Therefore, accurate fracture risk prediction is important. However, it is difficult for clinicians to differentiate between high and low fracture risk lesions, leading to considerable numbers of under and over treatment. To improve fracture risk prediction, we developed the BOS score; a patient-specific finite element (FE) computer model that calculates bone strength based on patient-specific anatomy and bone quality, obtained from CT scans.

Previously, we demonstrated that the BOS score improved fracture risk prediction in patients, compared to current clinical guidelines. The goal of this project is to initiate clinical implementation of the BOS score by determining whether the BOS score aids in fracture risk prediction and treatment decision making for patients with bone metastases in the femur. More information: website and animation below.

Researcher for this project: Florieke Eggermont.

Figure 1: BOS workflow. The physician sends the radiotherapy planning QCT scan together with some general patient information such as body weight to the Radboudumc. The CT scan is used to generate the FE model, on which an axial load is simulated until failure. The BOS score is calculated by normalizing bone strength by body weight. Subsequently, a report is returned, which can be used to determine the best treatment for patients.