Laia Humbert-Vidan

Publications

• COMPARISON OF DEEP-LEARNING DATA FUSION STRATEGIES IN MANDIBULAR OSTEORADIONECROSIS PREDICTION MODELLING USING CLINICAL VARIABLES AND RADIATION DOSE DISTRIBUTION VOLUMES
• Dentoalveolar radiation dose following IMRT in oropharyngeal cancer—An observational study
• Dynamic nomogram for long-term survival in patients with locally advanced oropharyngeal cancer after (chemo)radiotherapy
• Analyzing oropharyngeal cancer survival outcomes: A decision tree approach
• Comparison of machine learning methods for prediction of osteoradionecrosis incidence in patients with head and neck cancer
• National audit of a system for rectal contact brachytherapy
• In Regard to Reber et al
• Mathematical modelling of tumour volume dynamics in response to stereotactic ablative radiotherapy for non-small cell lung cancer
• Protocol Letter: A multi-institutional retrospective case-control cohort investigating PREDiction models for mandibular OsteoRadioNecrosis in head and neck cancer (PREDMORN)
• Prediction of Mandibular ORN Incidence from 3D Radiation Dose Distribution Maps Using Deep Learning
• External validation of a deep-learning mandibular ORN prediction model trained on 3D radiation distribution maps
• Mandibular dose-volume predicts time-to-osteoradionecrosis in an actuarial normal-tissue complication probability (NTCP) model: External validation of right-censored clinico-dosimetric and competing risk application across international multi-institutional observational cohorts and online graphical user interface clinical support tool assessment
• International Expert-Based Consensus Definition, Staging Criteria, and Minimum Data Elements for Osteoradionecrosis of the Jaw: An Inter-Disciplinary Modified Delphi Study
• Radiotherapy quadrant doses in oropharyngeal cancer treated with intensity modulated radiotherapy
• Comparison of Machine Leaning Models for Prediction of Acute Pain Severity and On-Treatment Opioid Utilization in Oral Cavity and Oropharyngeal Cancer Patients Receiving Radiation Therapy: Exploratory Analysis from a Large-Scale Retrospective Cohort
• Artificial Intelligence Uncertainty Quantification in Radiotherapy Applications - A Scoping Review
• A joint physics and radiobiology DREAM team vision – Towards better response prediction models to advance radiotherapy
• Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review
• EP-1929 Prediction of voxelwise mandibular osteoradionecrosis maps in HNC patients using deep learning
• PO-0967: Current practice in quality assurance of the Papillon50 contact X-ray brachytherapy system in the UK
• PO-0971 Locally advanced oropharyngeal cancer: a dynamic nomogram
• PH-0387 Mandible osteoradionecrosis: a dosimetric study
• PO-2107 Challenges in international real world evidence research collaboration. The PREDMORN experience
• Technical note: 9-month repositioning accuracy for functional response assessment in head and neck chemoradiotherapy
• EP-1603: Atlas of complication incidence to explore dosimetric contributions to osteoradionecrosis
• 2031: Multivariable analysis of mandibular osteoradionecrosis predictors: results from the PREDMORN study
• Computed tomography radiomics-based cross-sectional detection of mandibular osteoradionecrosis in head and neck cancer survivors
• Artificial intelligence uncertainty quantification in radiotherapy applications − A scoping review
• Comparison of deep-learning multimodality data fusion strategies in mandibular osteoradionecrosis NTCP modelling using clinical variables and radiation dose distribution volumes
• Deep Learning-Based Auto Contouring of Mandibular Sub-Volumes Based on the ClinRad System for Spatial Localization of Osteoradionecrosis of the Jaw
• External validation of a multimodality deep-learning normal tissue complication probability model for mandibular osteoradionecrosis trained on 3D radiation distribution maps and clinical variables
• Machine Learning Models Using Extended Clinical Variables to Predict Real-World Head and Neck Cancer Radiotherapy Toxicity
• Early Imaging Identification of Osteoradionecrosis and Classification Using the Novel ClinRad System: Results from A Retrospective Observational Cohort
• Multi-institutional Normal Tissue Complication Probability (NTCP) Prediction Model for Mandibular Osteoradionecrosis: Results from the PREDMORN Study
• Radiographic classification of mandibular osteoradionecrosis: A blinded prospective multi-disciplinary interobserver diagnostic performance study
• International Expert-Based Consensus Definition, Classification Criteria, and Minimum Data Elements for Osteoradionecrosis of the Jaw: An Interdisciplinary Modified Delphi Study
• Image-based Mandibular and Maxillary Parcellation and Annotation using Computer Tomography (IMPACT): A Deep Learning-based Clinical Tool for Orodental Dose Estimation and Osteoradionecrosis Assessment