Prognostication and local management in bone metastatic disease

This thesis focuses on the prognostication and treatment outcomes in patients suffering from bone metastases in different phases of the disease, which is crucial for the development of optimal screening and treatment plans.
In the first part the ability of spine surgeons to estimate the survival of patients with a spinal metastasis (without the use of prediction models) is assessed. Survival time is a crucial factor in the decision making process for these patients. The results of this study show that spine surgeons considerably overestimate a patients ‘survival while estimations do seem to be associated with treatment choice. Algorithms to help physicians in estimating survival may therefor proof to be beneficial. Part II focusses on breast cancer and the association of mutations status of genes TP₅₃ and PIK₃CA on metastatic behavior. The results show that TP₅₃ mutates breast cancer has a worse survival but may be less prone to develop bone metastasis. Part III builds on the results of part I, exploring new biomarkers for prognostication of patients with bone metastasis and developing machine learning algorithms to help physicians with survival prediction and thereby decision making for these patients.
In the final part of this thesis we look at the (adverse) treatment outcomes of frequently used treatment modalities for patients with a bone metastasis: radiation and surgery. Both modalities have their drawbacks and it is important to carefully weigh the benefits and risks, especially when life expectancy is short.