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Vienna, Austria

ESTRO 2025

Session

Machine learning models and clinical applications (excluding imaging and segmentation)
Digital Poster
Physics
Quantitative evaluation of a fully-automated planning solution for prostate-only and whole-pelvic radiotherapy
jessica prunaretty, France
E25-241
Clinical application of multiple target volumes auto-segmentation models which can accelerate the process of All-in-One radiotherapy
Luqi Wang, China
E25-1416
Clinical priorities-driven head and neck simultaneous integrated boost radiotherapy using deep reinforcement learning
jackie wu, USA
E25-1456
Enhancing the accuracy of respiratory-gated radiotherapy (RGRT) by using a hybrid deep-learning model to predict respiratory-induced organ motion
George (Guang) Li, USA
E25-1546
Deep-learning-based body-cavity segmentation to accurately preserve sliding motion in DIR-based super-resolution reconstruction of time-resolved 4DMRI
Harjinder Pawar, USA
E25-1558
Deep learning decision-making framework for optimal technique selection in breast radiotherapy: predicting dose distributions for IMRT and 3D-CRT
Helena Vivancos Bargalló, Spain
E25-1792
Estimating uncertainty for AI-based dose modelling in MRI-guided RT using ensemble networks, mean variance estimation and Monte Carlo dropout
Moritz Schneider, Germany
E25-1930
Complexity-based unsupervised machine learning for patient-specific VMAT quality assurance.
Savino Cilla , Italy
E25-1949
tackling missing values and imbalanced structured data using imputation techniques and synthetic data in colonoscopy prioritization
Shirin A. Enger, Canada
E25-1959
On the variation of plan quality due to epistemic uncertainty in dose prediction models
Anthony Carver, United Kingdom
E25-2043
ANALYZING LINGUAL MUSCLE COMPOSITION WITH ARTIFICIAL INTELLIGENCE (IA): AN INNOVATIVE APPROACH TO DYSPHAGIA ASSESSMENT IN HEAD AND NECK CANCER
Barbara Gabriela Salas Salas, Spain
E25-325
Robustness of predictive foundation model features in head and neck cancer
Andrew Hope, Canada
E25-2058
A clinical decision-making assistant for streamlining prostate MR-guided adaptive radiotherapy
Fanchi Su, USA
E25-2263
Data efficiency and prolonged prediction horizons for cyclic long short-term memory networks
Julius Arnold, Austria
E25-2340
Diffusion model-based medical image generation for artificial intelligence applications
Yibao Zhang, China
E25-2655
Artificial intelligence for in-vivo dosimetry using EPID in external beam photon radiotherapy
Lorenzo Marini, Italy
E25-2809
A new centre customizable Deep Learning approach for Patient-Specific Quality Assurance outcome prediction
Mauro Iori, Italy
E25-2900
retrospective analysis of the implementation of all-in-one radiotherapy in daily clinical practice
Jiazhou Wang, China
E25-3194
Combining different metastatic anatomical locations for the deep learning prediction of the monitor units per control point
Vanessa Panettieri, Australia
E25-3233
Outcomes for Human versus Machine Learning Prostate-Only Radiation Therapy Treatment Planning
Jeff Winter, Canada
E25-3304
Personalized Predictions of Neck Lymph Node Metastases in Head and Neck Cancer Using Mixture Hidden Markov Models
Yoel Samuel Pérez Haas, Switzerland
E25-498
Development and Validation of a Knowledge-Based Model for Automated CyberKnife Planning in Brain Lesions
SARA BROGGI, Italy
E25-3355
A unified deep-learning framework for enhanced patient-specific quality assurance
Hui Khee Looe, Germany
E25-3448
Automatic identification of trade-offs in head and neck cancer radiotherapy treatment planning
Hannu Laaksonen, Finland
E25-3600
Artificial Intelligence-driven failure prediction on TomoTherapy® systems
Kélian Poujade, France
E25-3604
Investigating the feasibility of deep-learning-based fast MRI-only proton range verification
Liheng Tian, Germany
E25-3625
A Digital Anatomy-Based Vertebra-Guided Affine Registration Network (VerGAReg)
peilin wang, Hong Kong (SAR) China
E25-3714
Cell survival predictions using machine learning and Monte Carlo simulations
andrea russomando, Chile
E25-3811
Decoding AI-adoption in radiotherapy: A multi-case qualitative comparative analysis
Martijn Vroegindeweij, Netherlands Antilles
E25-3864
Uncertainty quantification of a machine learning model for patient-specific quality assurance with conformal prediction
Nicola Lambri, Italy
E25-3932
A Deep Learning model for fast and accurate Oropharynx patients’ pretreatment positioning prediction in proton therapy
Hooman Bahrdo, The Netherlands
E25-3978
Data-Driven Refinement of Tumor Staging: A Hierarchical Clustering Approach for Prostate, Gastric, Liver, and Bladder Cancers
Bin Feng, China
E25-678
Synthetic image prediction for patient-specific quality assurance in stereotactic radiosurgery of multiple brain metastases
Nicola Lambri, Italy
E25-3982
A constraint programming approach for the radiotherapy scheduling problem
Hugues Rauwel, France
E25-4007
Quasi-3D virtual PSQA Using GAN-predicted 2D Dose Maps
Samuele Cavinato, Italy
E25-4057
Prospective clinical evaluation of fully automated contouring and treatment planning for prostate radiotherapy
David Neugebauer, Germany
E25-4168
MIND (Magnetic resonance Imaging Neural Dose calculator): Monte Carlo Level MRI-Only Proton Dose Calculation via Neural Networks
Ye Zhang, Switzerland
E25-4181
Evaluating uncertainty estimation models for clinical integration of AI-generated radiotherapy dose distributions
Kristen Duke, USA
E25-4371
A deep reinforcement learning approach for adaptive fractionation: Dynamic fraction size optimization for enhancing OAR sparing
Martin Weigand, The Netherlands
E25-4461
Early Error Detection in MR-Guided Online Adaptive Radiotherapy: A Peer Review Approach
Siqiu Wang, USA
E25-4519
A deep learning pipeline for real-time conformal palliative radiotherapy of spine metastases
Elsayed Ali, Canada
E25-759
Deep learning monitor units per control point prediction for automated VMAT treatment planning in prostate cancer
Mathieu Gaudreault, Australia
E25-1048
An Automated Patient-Specific Beam Angle Optimization Technique for Deep Learning Auto-Planning in Early Breast Cancer Treatment
Michele Zeverino, Switzerland
E25-1067
A Novel Beam-adaptive Efficient Monte Carlo Dose Calculation Engine via a Multi-modal Diffusion Model: Diff-MC
Jueye Zhang, China
E25-1213
An adaptive approach to enhance external-to-internal motion prediction by combining long short-term memory networks and time-domain cross-correlation
George (Guang) Li, USA
E25-1289