The delineation of the regions of interest (ROIs) plays an important role in the radiotherapy workflow. The ROIs include the gross tumor volume (GTV), the clinical target volume (CTV), the planning target volume (PTV) and organs at risk (OARs). There are however uncertainties related to the delineation of the ROIs due to inter­observer variability between radiation oncologists caused by e.g., a lack of consensus on anatomic definition or a lack of contrast in medical images. The largest inter­observer variability has been found in esophageal cancers, head and neck cancers, and lung cancers. To pre­ vent inter­observer variability, auto­contouring software can be used to delineate the ROIs. There are however also uncertainties in the delineations made by auto­contouring software. The purpose of this study was to perform an accurate evaluation of the dosimetric effect of delineation uncertainties. To do so, first a method to characterize the delineation uncertainties had to be found. The delineations uncertainties were characterized with principal component analysis (PCA). By performing PCA on a set of delineations, the eigenmodes which represent the variations between the delineations can be found and new random delineations based on the eigenmodes can be formed. To determine the dosimetric impact of the delineation uncertainties, two cases were investigated: (1) the dosimetric impact of delineation uncertainties for a fixed dose distribution; and (2) the dosimetric impact of delin­ eation uncertainties for a dose distribution reoptimized for every possible realization of a delineation. For the fixed dose distribution, Polynomial Chaos Expansion (PCE) was used as a meta­model for the dose volume histogram (DVH) of the target, and for the reoptimized dose distribution PCE served as a meta­model for the total dose distribution and the DVHs of the target and other ROIs. The delineation uncertainties of two data sets were analyzed: (1) 12 manual delineations of the GTV of a hepatocellular carcinoma patient; and (2) 90 auto­contours of the CTV and brainstem of a head and neck patient. The variation of the manual delineations of the GTV could be accurately described by 5 eigenmodes, while 45 eigenmodes were needed to describe the variations of the brainstem auto­ contours. The delineation uncertainties in the auto­contours of the CTV could not be characterized by PCA due to the shape of the CTV. The dosimetric effect of the delineation uncertainties of the manual delineations for a fixed dose distri­ bution was investigated for both an intensity modulated proton therapy (IMPT) plan and a volumetric­ modulated arc therapy (VMAT) plan for 10,000 random delineations of the CTV. For this patient the CTV was equal to the GTV. In the VMAT plan, the CTV received sufficient dose for all delineations, but the PTV was underdosed in 17.1% of the delineations. In the IMPT plan, the CTV was underdosed in 69.0% of the delineations. This percentage of underdosed CTV delineations in the IMPT plan was reduced when the plan was made robustly. The results for the fixed IMPT dose distribution should however be verified with a more accurate PCE model. A proof of principle for the PCE as a meta­model for the reoptimized dose distribution and DVHs of the target and other ROIs was shown. However, a more accurate PCE model with a higher polynomial order would be needed to analyze the effects of the reoptimization on the dose delivered to the ROIs. It has been shown that the dosimetric impact of delineation uncertainties can be modelled using PCE. This is a first step towards systematically and quantitatively taking into account delineation uncertainties in radiotherapy treatment planning. In future research the analysis of the dosimetric impact of the un­ certainties can e.g., be used in adaptive radiotherapy in which auto­contouring is used. By knowing the dosimetric impact of the uncertainty, treatment plans could be optimized such that a structure receives its target dose with a certain probability or critical areas where the dosimetric impact of the delineation uncertainties is large could be flagged such that these areas are checked before treatment.