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Vienna, Austria

ESTRO 2023

Session

Automation
6028
Poster (Digital)
Physics
Head and Neck high-risk lymph nodes detection – a three-dimensional deep learning proposal
YungFa Lu, Taiwan
PO-1614
Automated treatment planning – Three-year clinical summary of dosimetry and user experience
jackie wu, USA
PO-1615
The importance of regional analysis in contour comparison
Hannah Chamberlin, United Kingdom
PO-1616
Multicentric evaluation of a machine learning model to streamline the RT patient-specific QA process
Nicola Lambri, Italy
PO-1617
Automation of the Picket Fence quality control test through digital image processing
Rodrigo Astudillo, Spain
PO-1618
AI-based classifier using geometrical features to minimize heart dose in left breast with VMAT
Andrea Bresolin, Italy
PO-1619
Transferability of deep learning models to the segmentation of gross tumour volume in brain cancer
Emiliano Spezi, United Kingdom
PO-1620
Evaluation of a CT scanner based deep learning auto-contouring solution for lung radiotherapy
Matthew Williams, United Kingdom
PO-1622
MCO-based training of RapidPlan for lung cancer improves sparing of heart and oesophagus
Liv Bolstad Hysing, Norway
PO-1623
Reproducibility of AI-based contour generation on synthetic CT
Ivan Coric, Germany
PO-1625
Interobserver study of deep learning-based segmentation for nodal target volumes in breast cancer
Nienke Hoekstra, The Netherlands
PO-1627
Dosimetric Evaluation of MR-based Deep Learning Automatic Contouring in the Pelvis
JJ Wyatt, United Kingdom
PO-1628
Lexicographic optimization-based planning for brain metastasis radiosurgery with coplanar arcs
Sara Trivellato, Italy
PO-1630
Commercial deep learning-based automated treatment planning validation for oropharyngeal cancer
Frank Dankers, The Netherlands
PO-1631
deep learning-based automatic segmentation of rectal tumors in endoscopy images
Alana Thibodeau-Antonacci, Canada
PO-1632
Clinical evaluation of deep learning-based nodal structures segmentation for gynecological cancers
Shrikant Deshpande, Australia
PO-1633
Validation of a U-Net-based algorithm for MRI-guided extremity soft tissue sarcoma GTV segmentation
Lucas Zander, Germany
PO-1634
Evaluation of CBCT-based auto contouring for online adaptive radiotherapy of cervical cancer
Catherine Khamphan, France
PO-1635
Comprehensive analysis of different commercial auto-segmentation tools for multi-site OAR contouring
Gerd Heilemann, Austria
PO-1636
Deep learning-based IMRT treatment planning on synthetic-CT for ART in NSCLC-patients
Dylan Callens, Belgium
PO-1637
Physician-specific preferences yield minor differences in knowledge-based planning generated plans
Robert Kaderka, USA
PO-1638
Autosegmentation of structures for Cranial Spinal Irradiation patients using Deep Learning
Jesper Kallehauge, Denmark
PO-1639
MRI-based deep learning autocontouring: Evaluation and implementation for brain radiotherapy
Nouf Alzahrani, United Kingdom
PO-1640
Clinical evaluation of autosegmentation using AI with manual segmentation of breast tissue
Remus-Cosmin Stoica, Romania
PO-1641
Towards automated treatment planning for robotic stereotactic radiosurgery
Marcel Nachbar, Germany
PO-1643
Robustness of nodal outlining software for breast radiotherapy
Alison McBride, United Kingdom
PO-1644
Deep learning and atlas-based approaches for Total Marrow and Lymphoid Irradiation segmentation
Damiano Dei, Italy
PO-1645
KB plan optimization models’ transferability: multi-institutional international consortia validation
Lorenzo Placidi, Italy
PO-1646
Novel dataset validation of deep learning models for autocontouring of head and neck, and prostate
Daniel Sandys, United Kingdom
PO-1647
Deep Learning-Based Automatic Segmentation for Brain OARs: Accuracy and Dosimetric Impact
Andrada Turcas, Romania
PO-1648
Style-based generative model to reconstruct head and neck 3D CTs
Alexandre Cafaro, France
PO-1649
A 3D transfer learning approach for identifying multiple simultaneous errors during radiotherapy
Cecile Wolfs, The Netherlands
PO-1650
Inter-institute transferability of KB plan prediction models for FB LWB on DIBH candidates
Alessia Tudda, Italy
PO-1651
KB multi-institutional plan prediction of the left whole breast irradiation with tangential fields
Alessia Tudda, Italy
PO-1652
Auto-contouring of cardiac avoidance region for cardiac sparing lung radiotherapy
Tom Marchant, United Kingdom
PO-1653
Does data curation matter in deep learning segmentation? Clinical vs edited GTVs in glioblastoma.
Kim Hochreuter, Denmark
PO-1654
Multi-center auto-segmentation model for internal mammary nodes using clinical data: A DBCG study
Emma Skarsø Buhl, Denmark
PO-1655
autoencoder-based quality assurance of deep learning segmentation of parotid glands in HNC patients
Alessia De Biase, The Netherlands
PO-1656
Clinical validation of AI-based CT auto-contouring for prostate cancer Radiotherapy
Claudio Fiorino, Italy
PO-1657
The impact of LIMBUS AI based contouring on the efficiency of prostate radiotherapy planning
Kishen Patel, United Kingdom
PO-1659
Automated Clinical Treatment Planning for breast: from manual to auto planning in Clinical Practise
Anna Vella, United Kingdom
PO-1660
AI-based auto-segmentation: advantages in delineation, absorbed dose-distribution and logistics
Gustavo R. Sarria, Germany
PO-1662
When no news is good news: commercial automated EPID in vivo dosimetry deployed as a safety check
Rhydian Caines, United Kingdom
PO-1665
Automated clinical-criteria-driven optimal planning: clinical experience with over 6000 patients
Linda Hong, USA
PO-1666