Page 126 - Predicting survival in patients with spinal bone metastasesL

CHAPTER IX
Society of Clinical Oncology, demonstrating the use of IBM’s Watson for Oncology (WFO), a self-learning computing system that is able to provide evidence-based treatment recommendations, by processing data from medical literature, treatment guidelines, imaging, laboratory results and pathology reports, among many other sources. Studies showed a high concordance between recommendations made by tumor boards and WFO.27,28 Also, recent work done by Groenen et al. made use of finite element modelling to assess spinal stability.29 This is a form of structural stress analysis developed in engineering mechanics and can be applied to CT-based assessments of the metastatic spine. By further developing this technique, perhaps in combination with machine learning, a more robust system for predicting spinal instability can be created.30,31
Over the past years, advances in radiotherapy planning techniques as well as advances in minimally invasive surgical techniques have opened up entire new avenues of treatment. However, in order to adequately deploy these new treatment strategies, correct assessment of spinal stability, as well as survival is essential in order to avoid overtreatment. In the case of Stereotactic Body Radiotherapy (SBRT), the benefit of a faster and longer lasting response must be offset to the higher risk of inducing vertebral compression fractures (VCFs). While this complication might be an acceptable risk in a curative or oligometastatic setting, in a palliative setting, it is not. Ideally, these treatment decisions are made in a multidisciplinary setting, including all medical specialties involved in diagnosing and treating spinal metastases.
This thesis provides an insight into the challenges of selecting the appropriate treatment for patients presenting with spinal metastases, by considering the estimated survival and clinical assessment of spinal instability. Hopefully this work can be used to further improve patient and treatment selection and further reduce overtreatment of patients in a palliative setting.
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