In this thesis we assessed the role of functional MRI and molecular imaging in head and neck squamous cell cancer (HNSCC). With the use of dynamic contrast-enhanced (DCE) MRI, intravoxel incoherent motion (IVIM), diffusion-weighted (DW) MRI and 18F-FDG-PET/CT imaging techniques we aimed to improve the diagnostic and prognostic accuracy. First we assessed the validity of relatively new imaging techniques; IVIM MRI and DCE and patient adherence. In Chapter 2.1, we concluded that the repeatability from the Bayesian and neural network approaches are superior to that of nonlinear regression for estimating IVIM model parameters. In Chapter 2.2, the use of a population averaged AIF seems to be preferable for pharmacokinetic analysis using DCE-MRI in the head and neck area. In Chapter 2.3, The drop-out frequency of advanced-staged HNSCC patients for imaging during (chemo)radiotherapy in a research-setting was high and mainly patient-related. Secondly, we explored the potential application of IVIM and subsequently we aimed to improve diagnostic workup of patients presenting with an unknown primary tumor and detection in post-treatment patients with risk of treatment failure. In Chapter 3.1, according to our review the combination of IVIM parameters enables a reliable differentiation between various types of tumors. In Chapter 3.2, a high sensitivity for diagnostic functional imaging (DWI and 18F-FDG-PET/CT) to detect an unknown primary tumor; even with state of the art diagnostic imaging the primary tumor location remained obscure in 15 (48%) of the included patients. In Chapter 3.3, ultrafast-DCE showed potential to improve detection of unknown primary tumors in addition to DWI and 18F-FDG-PET/CT in patients with cervical squamous cell carcinoma lymph node metastasis. The combined use of ultrafast-DCE, DWI and 18F-FDG-PET/CT yielded highest sensitivity. In Chapter 3.4, a sequential approach including both qualitative analysis of DWI and 18F-FDG-PET/CT resulted in the best diagnostic accuracy for follow-up after (chemo)radiotherapy. Thirdly, we focused on the prognostic accuracy of pretreatment functional MRI and molecular imaging techniques to assess locoregional recurrence, distant metastasis and overall survival. In Chapter 4.1, low pre-treatment D and f and an increase in D during treatment were associated with a favorable response to treatment. In Chapter 4.2, both DWI- and 18F-FDG-PET/CT-parameters appear to have predictive value for treatment failure, locoregional recurrence and death. Combining SUVmax-PT and ADCmax-PT resulted in better prediction of treatment failure compared to single parameter assessment. In Chapter 4.3, a combination of HPV-status, first-order 18F-FDG-PET parameters and complementary radiomic factors was most accurate for locoregional recurrence, distant metastasis and adverse overall survival prediction. Furthermore, predictive phenotype-specific tumor characteristics and interactions might be captured and retained in radiomic factors, which allows for personalized risk stratification and optimizing personalized cancer care. In Chapter 4.4, the combination of clinical parameters, HPV-status, with DCE-, IVIM-MRI and 18F-FDG-PET/CT, provided complementary value in capturing tumor characteristics and improved prediction of locoregional recurrence-free survival and overall survival. Finally, we systematically reviewed the literature and prospectively assessed the prognostic capacity of functional imaging before and early after start of treatment with (chemo)radiotherapy. In Chapter 5.1, various functional and molecular imaging parameters were found to have a role in prediction response to treatment and locoregional control. In Chapter 5.2, intratreatment functional imaging parameters capture early tumoral changes that provide only prognostic information regarding LRRFS. The best prognostic models are for LRRFS a combination of pretreatment, intratreatment and Δ-functional imaging parameters; for DMFS only pretreatment functional imaging-parameters, and for OS a combination of HPV-status, gender and only pretreatment functional-imaging parameters. Accurate clinically applicable risk stratification calculators may enable personalized treatment (adaptation) management early during-treatment, improve counselling and enhance patient-specific post-therapy monitoring.
|Qualification||Doctor of Philosophy|
|Award date||23 Sept 2022|
|Place of Publication||Amsterdam|
|Publication status||Published - 23 Sept 2022|
- DWI, DCE, MRI, 18F-FDG-PET, radiomics, machine learning, head neck cancer