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: Histopathological examination in the diagnostic workflow of oropharyngeal squamous cell carcinoma (OPSCC) is essential. We aimed to develop a machine learning pipeline to predict human papillomavirus (HPV) status in OPSCC patients based on clinical variables and multiparametric magnetic resonance imaging (MRI).
Methods: In a dataset of OPSCC patients (n=59), we extracted features from three categories: clinical variables; histogram parameters from diffusion weighted imaging (DWI)-MRI model; radiomics based on T2-weighted and DWI-MRI. We performed ten-times repeated stratified five-fold cross-validation and divided each outer training set (80%) into an inner training set (80% of outer training set) and validation set (20% of outer training set) using five-fold stratified cross-validation. We performed three types of feature selection methods (LASSO, statistical analysis and manual selection), tuned and trained seven classifiers (logistic regression, k-nearest-neighbours, naive bayes, random forest, support vector machine, XGBoost and LightGBM) to find the optimal combination of features, hyperparameters and classifier on each inner training set. We ensembled the inner fold models to fit on the outer training set and tested on the outer test set (20%). We constructed additional models with subsets of the features.
Results: the combined model area under the curve was 0.793±0.136. Models including clinical features outperformed models without clinical features (p<0.001). Features from all feature categories were selected for the combined model.
Conclusion: we were able to predict HPV status in OPSCC patients using multiparametric MRI and clinical variables with reasonable accuracy, though retraining and validating on larger, external datasets is needed before implementation in clinic.

Background and purpose: In intensity modulated proton therapy (IMPT), the impact of setup errors and anatomical changes is commonly mitigated by robust optimization with population-based setup robustness (SR) settings and offline replanning. In this study we propose and evaluate an alternative approach based on daily plan selection from patient-specific pre-treatment established plan libraries (PLs). Clinical implementation of the PL strategy would be rather straightforward compared to daily online re-planning. Materials and methods: For 15 head-and-neck cancer patients, the planning CT was used to generate a PL with 5 plans, robustly optimized for increasing SR: 0, 1, 2, 3, 5 mm, and 3% range robustness. Repeat CTs (rCTs) and realistic setup and range uncertainty distributions were used for simulation of treatment courses for the PL approach, treatments with fixed SR (fSR₃) and a trigger-based offline adaptive schedule for 3 mm SR (fSR₃OfA). Daily plan selection in the PL approach was based only on recomputed dose to the CTV on the rCT. Results: Compared to using fSR₃ and fSR₃OfA, the risk of xerostomia grade ≥ II & III and dysphagia ≥ grade III were significantly reduced with the PL. For 6/15 patients the risk of xerostomia and/or dysphagia ≥ grade II could be reduced by > 2% by using PL. For the other patients, adherence to target coverage constraints was often improved. fSR₃OfA resulted in significantly improved coverage compared to PL for selected patients. Conclusion: The proposed PL approach resulted in overall reduced NTCPs compared to fSR₃ and fSR₃OfA at limited cost in target coverage.

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