Lisanne van Dijk

Publications

• Black bone MRI morphometry for mandibular cortical bone measurement in head and neck cancer patients: Prospective method comparison with CT
• Precision association of lymphatic disease spread with radiation-associated toxicity in oropharyngeal squamous carcinomas
• Tumor Segmentation in Patients with Head and Neck Cancers Using Deep Learning Based-on Multi-modality PET/CT Images
• Normal Tissue Complication Probability (NTCP) prediction model for osteoradionecrosis of the mandible in head and neck cancer patients following radiotherapy: Large-scale observational cohort
• The impact of induction and/or concurrent chemoradiotherapy on acute and late patient‐reported symptoms in oropharyngeal cancer: Application of a mixed‐model analysis of a prospective observational cohort registry
• Optimal policy determination in sequential systemic and locoregional therapy of oropharyngeal squamous carcinomas: A patient-physician digital twin dyad with deep Q-learning for treatment selection
• Normal Tissue Complication Probability (NTCP) Prediction Model for Osteoradionecrosis of the Mandible in Patients With Head and Neck Cancer After Radiation Therapy: Large-Scale Observational Cohort
• Progression Free Survival Prediction for Head and Neck Cancer using Deep Learning based on Clinical and PET-CT Imaging Data
• Head and Neck Cancer Primary Tumor Auto Segmentation using Model Ensembling of Deep Learning in PET-CT Images
• Combining Tumor Segmentation Masks with PET/CT Images and Clinical Data in a Deep Learning Framework for Improved Prognostic Prediction in Head and Neck Squamous Cell Carcinoma
• Head and Neck Cancer Primary Tumor Auto Segmentation Using Model Ensembling of Deep Learning in PET/CT Images
• Progression Free Survival Prediction for Head and Neck Cancer Using Deep Learning Based on Clinical and PET/CT Imaging Data
• Combining Tumor Segmentation Masks with PET/CT Images and Clinical Data in a Deep Learning Framework for Improved Prognostic Prediction in Head and Neck Squamous Cell Carcinoma
• Optimal Treatment Selection in Sequential Systemic and Locoregional Therapy of Oropharyngeal Squamous Carcinomas: Deep Q-Learning With a Patient-Physician Digital Twin Dyad (Preprint)
• Optimal Treatment Selection in Sequential Systemic and Locoregional Therapy of Oropharyngeal Squamous Carcinomas: Deep Q-Learning With a Patient-Physician Digital Twin Dyad
• Head and Neck Cancer Predictive Risk Estimator to Determine Control and Therapeutic Outcomes of Radiotherapy (HNC-PREDICTOR): Development, international multi-institutional validation, and web-implementation of clinic-ready model-based risk stratification for head and neck cancer
• Deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for predicted tumor probability in FDG PET and CT images
• Temporal Characterization of Longitudinal Sequelae Including Acute Pain, Physiologic Status, and Toxicity Kinetics in Head and Neck Cancer Patients Receiving Radiotherapy: A Prospective Electronic Health Record Embedded Registry Study.
• THALIS: Human-machine analysis of longitudinal symptoms in cancer therapy
• Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approach
• Evaluation of deep learning-based multiparametric MRI oropharyngeal primary tumor auto-segmentation and investigation of input channel effects: Results from a prospective imaging registry
• Dysphagia and shortness-of-breath as markers for treatment failure and survival in oropharyngeal cancer after radiation
• Proton Image-guided Radiation Assignment for Therapeutic Escalation via Selection of locally advanced head and neck cancer patients [PIRATES]: A Phase I safety and feasibility trial of MRI-guided adaptive particle radiotherapy
• Auto-Detection and Segmentation of Involved Lymph Nodes in HPV-Associated Oropharyngeal Cancer Using a Convolutional Deep Learning Neural Network
• Improved Xerostomia Prediction in Head and Neck Cancer Patients with Dixon Magnetic Resonance Imaging of Glandular Adiposity: Validation of Semi-Quantitative Parotid T1 Signal Intensity Metrics for Biomarker Pre-Qualification
• Optimized decision support for selection of transoral robotic surgery or (chemo)radiation: Quantified pre-therapy risk stratification for patient-reported and clinician-graded swallowing impairment and toxicity
• MRI intensity standardization evaluation design for head and neck quantitative imaging applications
• Hypomagnesemia and incidence of osteoradionecrosis in patients with head and neck cancers
• External validation of nodal failure prediction models including radiomics in head and neck cancer
• Comparison of Machine-Learning and Deep-Learning Methods for the Prediction of Osteoradionecrosis Resulting From Head and Neck Cancer Radiation Therapy
• Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients
• Identifying Symptom Clusters Through Association Rule Mining
• 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
• NTCP modeling of late effects for head and neck cancer: A systematic review
• MR-Guided Adaptive Radiotherapy for OAR Sparing in Head and Neck Cancers
• Development and validation of a contouring guideline for the taste bud bearing tongue mucosa
• SLICE-BY-SLICE DEEP LEARNING AIDED OROPHARYNGEAL CANCER SEGMENTATION WITH ADAPTIVE THRESHOLDING FOR SPATIAL UNCERTAINTY ON FDG PET AND CT IMAGES
• The Importance of Radiation Dose to the Atherosclerotic Plaque in the Left Anterior Descending Coronary Artery for Radiation-Induced Cardiac Toxicity of Breast Cancer Patients?
• THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy
• Auto-detection and segmentation of involved lymph nodes in HPV-associated oropharyngeal cancer using a convolutional deep learning neural network
• Advances in Imaging for HPV-Related Oropharyngeal Cancer: Applications to Radiation Oncology
• Semi-automated 18F-FDG PET segmentation methods for tumor volume determination in Non-Hodgkin lymphoma patients: a literature review, implementation and multi-threshold evaluation
• Validation of the 18F-FDG PET image biomarker model predicting late xerostomia after head and neck cancer radiotherapy
• Can we safely reduce the radiation dose to the heart while compromising the dose to the lungs in oesophageal cancer patients?
• Intensity standardization methods in magnetic resonance imaging of head and neck cancer
• Protocol Letter: A multi-institutional retrospective case-control cohort investigating PREDiction models for mandibular OsteoRadioNecrosis in head and neck cancer (PREDMORN)
• Dysphagia and shortness-of-breath as markers for treatment failure and survival in oropharyngeal cancer after radiation
• Development of a high-performance multiparametric MRI oropharyngeal primary tumor auto-segmentation deep learning model and investigation of input channel effects: Results from a prospective imaging registry
• Predicting late symptoms of head and neck cancer treatment using LSTM and patient reported outcomes
• Optimized decision support for selection of transoral robotic surgery or (chemo)radiation therapy based on posttreatment swallowing toxicity
• Radiomic biomarkers of tumor immune biology and immunotherapy response
• Robust Intensity Modulated Proton Therapy (IMPT) increases estimated clinical benefit in head and neck cancer patients
• Selection of head and neck cancer patients for adaptive radiotherapy to decrease xerostomia
• Meeting the Challenge of Scientific Dissemination in the Era of COVID-19: Toward a Modular Approach to Knowledge-Sharing for Radiation Oncology
• Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology
• Imaging for Response Assessment in Radiation Oncology: Current and Emerging Techniques
• Development and evaluation of an auto-segmentation tool for the left anterior descending coronary artery of breast cancer patients based on anatomical landmarks
• Predicting response to neoadjuvant chemoradiotherapy in esophageal cancer with textural features derived from pretreatment 18F-FDG PET/CT imaging
• Precision toxicity correlates of tumor spatial proximity to organs at risk in cancer patients receiving intensity-modulated radiotherapy
• Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring
• Limited Impact of Setup and Range Uncertainties, Breathing Motion, and Interplay Effects in Robustly Optimized Intensity Modulated Proton Therapy for Stage III Non-small Cell Lung Cancer
• Improving the prediction of overall survival for head and neck cancer patients using image biomarkers in combination with clinical parameters
• Geometric Image Biomarker Changes of the Parotid Gland Are Associated With Late Xerostomia
• 18F-FDG PET image biomarkers improve prediction of late radiation-induced xerostomia
• Parotid gland fat related Magnetic Resonance image biomarkers improve prediction of late radiation-induced xerostomia
• Validation and modification of a prediction model for acute cardiac events in patients with breast cancer treated with radiotherapy based on three-dimensional dose distributions to cardiac substructures
• Normal tissue complication probability (NTCP) models for late rectal bleeding, stool frequency and fecal incontinence after radiotherapy in prostate cancer NTCP models for anorectal side effects patients
• The image biomarker standardization initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping
• Technical Note: Extension of CERR for computational radiomics: A comprehensive MATLAB platform for reproducible radiomics research
• Reply letter to “Texture analysis of parotid gland as a predictive factor of radiation induced xerostomia: A subset analysis”
• CT image biomarkers to improve patient-specific prediction of radiation-induced xerostomia and sticky saliva
• Development of a prediction model for late urinary incontinence, hematuria, pain and voiding frequency among irradiated prostate cancer patients
• The prognostic value of CT-based image-biomarkers for head and neck cancer patients treated with definitive (chemo-)radiation
• Pre-treatment radiomic features predict individual lymph node failure for head and neck cancer patients
• Delta-radiomics features during radiotherapy improve the prediction of late xerostomia
• DASS Good: Explainable Data Mining of Spatial Cohort Data
• Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas
• Population-Based External Validation of the EASIX Scores to Predict CAR T-Cell-Related Toxicities
• Optimized decision support for selection of transoral robotic surgery or (chemo)radiation therapy based on posttreatment swallowing toxicity
• Multi-organ spatial stratification of 3-D dose distributions improves risk prediction of long-term self-reported severe symptoms in oropharyngeal cancer patients receiving radiotherapy: development of a pre-treatment decision support tool
• Head and Neck Radiation Therapy Patterns of Practice Variability Identified as a Challenge to Real-World Big Data: Results From the Learning from Analysis of Multicentre Big Data Aggregation (LAMBDA) Consortium
• Slice-by-slice deep learning aided oropharyngeal cancer segmentation on PET and CT images
• Big data prediction models to select head and neck patients for personalized dose prescription
• External validation of osteoradionecrosis NTCP models in head and neck cancer patients
• CT-based deep multi-label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma
• Temporal characterization of acute pain and toxicity kinetics during radiation therapy for head and neck cancer
• MO-0960 Evaluating semi-automated 18F-FDG PET segmentation methods to predict Large B-cell lymphoma outcomes
• Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer
• Deep learning-based outcome prediction using PET/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer
• Reduction of Metabolic Active Tumor Volume Prior to CAR T-Cell Therapy Improves Survival Outcomes in Patients with Large B-Cell Lymphoma
• Parotid gland fat related Magnetic Resonance Image biomarkers improve prediction of late xerostomia
• Geometric image biomarker changes at the third treatment week predict late xerostomia
• Predicting salivary gland dysfunction with image biomarkers in head and neck cancer patients
• MR-only guided proton therapy
• Validation and Optimization of a Cardiac NTCP Model for Breast Cancer Patients Based on Cardiac Substructures and 3-dimensional Dose Distributions
• Prognostic Image Biomarkers for Nasopharyngeal Cancer Patients Treated With (Chemo)Radiation
• A single dose distribution per plan to represent possible target underdosage in a multi-scenario robustness evaluation
• Pre-treatment radiomic features predict individual nodal failure in head and neck cancer
• MR-Delta image biomarkers to identify partial HNC responders that advance to complete responders
• TransRP: Transformer-based PET/CT feature extraction incorporating clinical data for recurrence-free survival prediction in oropharyngeal cancer
• METhodological RadiomICs Score (METRICS)
• Uncertainty-Aware Deep Learning for Segmentation of Primary Tumour and Pathologic Lymph Nodes in Oropharyngeal Cancer: Insights from a Multi-Centre Cohort
• Reducing and controlling metabolic active tumor volume prior to CAR T-cell infusion can improve survival outcomes in patients with large B-cell lymphoma
• International Expert-Based Consensus Definition, Staging Criteria, and Minimum Data Elements for Osteoradionecrosis of the Jaw: An Inter-Disciplinary Modified Delphi Study