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Publicaties

Normal tissue complication probability model predicting taste impairment in head and neck cancer patients: Evaluating the taste bud bearing tongue mucosa as a predictor

Three-Dimensional Deep Learning Normal Tissue Complication Probability Model to Predict Late Xerostomia in Patients With Head and Neck Cancer

Cluster-Based Toxicity Estimation of Osteoradionecrosis Via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability by Using Whole Dose-Volume Histograms for Cohort Risk Stratification

Deep learning-based outcome prediction using PET/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer

Future of Team-based Basic and Translational Science in Radiation Oncology

Irradiation of organs at risk may have adverse effects on survival in breast cancer patients

Late-xerostomia prediction model based on 18F-FDG PET image biomarkers of the main salivary glands

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII

PET/CT based transformer model for multi-outcome prediction in oropharyngeal cancer

Predicting late taste loss with deep learning NTCP model using 3D dose, CT and OAR segmentations

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