Predicting salivary gland dysfunction with image biomarkers in head and neck cancer patients

This thesis is the first to show that medical images can be used to improve prediction models for radiation-induced xerostomia, the syndrome of dry mouth, in head and neck cancer patients. Better life expectancy of HNC survivors has led to an increased demand for prediction, prevention and understanding of the development treatment-induced side effects. In addition, more advanced treatment have become available, such as proton therapy, that have great potential to spare normal tissues, giving rise to multiple treatment options . The image characteristics that are extracted represent patient-specific tissue characteristics that are quantified in tangible values, allowing for quantitative analysis of three-dimensional clinical image information. We developed dedicated software to extract image characteristics from clinical images. Xerostomia prediction was improved by the addition of normal tissue image characteristics, which were either extracted before, during or after radiotherapy, to reference prediction models that were based on radiation dose parameters and baseline side effects scores only. By optimizing side effect prediction, this thesis contributes to the next step in personalized treatment approaches. Furthermore, it generated hypotheses for the patient-specific reaction to radiation dose, hereby advancing towards a better understanding of the development of late treatment-induced toxicities.