University of Groningen
Predicting salivary gland dysfunction with image biomarkers in head and neck cancer patients
van Dijk, Lisanne Vania
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Publication date: 2018
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
van Dijk, L. V. (2018). Predicting salivary gland dysfunction with image biomarkers in head and neck cancer patients. Rijksuniversiteit Groningen.
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Stellingen
1. Image biomarkers derived from CT-, PET- and MR- images of the parotid glands can be used to improve NTCP models for the prediction of late radiation-induced xerostomia. (This thesis) 2. High fat concentrations within the parotid glands, quantified with MR-image biomarkers, are
related to a higher risk of developing xerostomia 12 months after radiotherapy (this thesis) 3. Changes in normal tissue image biomarkers that are correlated to radiation dose do not
necessarily correlate with patient-rated complications after radiotherapy. (this thesis) 4. Geometric changes of the parotid glands after treatment can accurately predict whether or
not patients recover from acute xerostomia within 12 months of treatment (This thesis) 5. Image biomarker changes at week 3 during treatment can be used to guide treatment
adaptation to decrease the risk of late xerostomia, by, for example, changing from photon to proton therapy (this thesis)
6. There are more statistical roads that lead to the same model 7. Images are more than pictures they are data (Robert J. Gillies)
8. Imperfect prediction, despite being imperfect, can be valuable for decision-making purposes. (Michael Kattan)
9. Well-behaved women seldom make history. (Laurel T. Ulrich)
10. If you thought that science was certain - well, that is just an error on your part. (Richard P. Feynman)
