Background: Oropharyngeal squamous cell carcinoma (OPSCC) not associated with the human papillomavirus (HPV) have a poor prognosis compared to their HPV-associated counterpart. In literature diffusion-weighted imaging (DWI) shows great potential as a biomarker for treatment response in HPV-associates OPSCC. For this study the possibility of using DWI parameters as biomarker for treatment response during treatment of HPV-negative OPSCC is investigated. Tumor size and shrinkage have also been investigated.
Methods: In this study sixteen patients with HPV-negative OPSCC who underwent (chemo-)radiotherapy have been included. All are current and/or former smokers. At two time point an DWI-MRI scan took place, before and 2 weeks into treatment. The mean apparent diffusion coefficient (ADC) and Non-Gaussian Intravoxel Incoherent Motion Imaging (NG-IVIM) parameters based on the gross tumor volume (GTV) have been calculated. The parameters and differences in parameters have been compared between patients that responded well to treatment and patients with progressive disease.
Results: At pre- and midtreatment no significant differences have been found between the Complete Response (CR) and Progressive Disease(PD) patient groups. Diffusion coefficient D showed a significant change between pre- and midtreatment, with an increase for PD and decrease for CR. When comparing the residual region to the region that had disappeared after two weeks based on pretreatment data significant differences have been found in ADC, D and f for CR, as well as f for PD.
Conclusion: Changes in D during treatment have shown to be a significant predictor for treatment response. However this study is limited by a small patient group and more research with larger cohorts and additional biomarkers is needed to develop reliable clinical decision-making tools.

Background: Glioma is a type of brain, originating from glial cells that support nerve cells, such as astrocytes and dendrocytes. It varies in type and severity, often resulting in a low life expectancy due to its aggressive nature. Since the implementation of the 2016 WHO Central Nervous System tumor classification, Isocitrate Dehydrogenase (IDH) mutation status has been used as a marker to distinguish the severity of the gliomas. This research explores the correlation between Arterial Spin Labeling (ASL) and Dynamic Susceptibility Contrast (DSC) MRI techniques to determine IDH mutation status in non-enhancing gliomas. Methods: This project used 34 iGene patient dataset. To overlay ASL to DSC, both images were registered to the FLAIR images as the registration reference. The statistical analysis was done by dividing the tumor region for both ASL and DSC into three areas: hypo-perfusion, iso-perfusion, and hyperperfusion. Then, the images were flattened from 3D to 1D data and the statistical analysis was done with Spearman’s correlation. The correlation between those three areas for two imaging techniques resulted in nine cases. To test the interclass significance, the Mann Whitney-U test was used. Results: This project found a higher mismatch area between ASL-CBF and DSC-rCBV in the IDHwildtype group compared to the IDH-mutant group. Additionally, the mismatch area in IDH-wildtype shows a lower Spearman’s coefficient, suggesting different information captured by those two imaging techniques. It reveals significant findings that both ASL-CBF and DSC-rCBV can be valuable in distinguishing IDH mutation statuses, with notable differences in perfusion characteristics between IDH-mutant and IDH-wildtype gliomas. Conclusion: Voxel-wise correlation between ASL-CBF and DSC-rCBV can be a potential marker to distinguish IDH-mutant from IDH-wildtype.

Long acquisition times impede the routine clinical use of quantitative magnetic resonance imaging (qMRI). qMRI quantifies meaningful tissue parameters in T1-, T2-, and PD-maps, as opposed to conventional (qualitative) weighted MRI (wMRI), which only visualises contrast between tissues. Although methods exist that generate synthetic wMRI from qMRI, the inverse problem has not been thoroughly studied yet. A method to generate qMRI from wMRI would be beneficial as it does not change current clinical workflows and enables retrospective quantitative analysis. This thesis investigates to what extent fully convolutional networks are successful in generating qMRI from T1-weighted, T2-weighted, PD-weighted and T2-weighted-FLAIR scans. A set of synthetic wMRI scans from 97 healthy volunteers was split into training, validation and test sets for development of our models. We varied model architectures, loss functions and learning rates during training, in order to find the best performing models. These were able to predict qMRI with median errors of approximately 5% on the test set. Additionally, we determined the amount of information contained in the input scans by training models using different combinations of the input. These results showed that T1-weighted, T2-weighted and PD-weighted scans were the most important. Models trained on synthetic wMRI were tested on an additional dataset of real wMRI. This resulted in higher median errors of 27.4%, 12.0% and 8.7% for T1-, T2- and PD-maps respectively. Furthermore, the same models were tested on a third dataset of synthetic tumour scans and mainly showed errors around the tumour core. These results show that more research is necessary in order to improve the performances of models generating qMRI to a clinical standard.