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Vienna, Austria

ESTRO 2023

Session

Radiomics, modelling and statistical methods
Poster (Digital)
Physics
Deep learning-based classification of Xerostomia in ARTSCAN III patients
Viktor Rogowski, Sweden
PO-2083
A radiobiological model of the synergy between radiotherapy and immunotherapy
Isabel Gonzalez-Crespo, Spain
PO-2085
NTCP modelling with dosiomics features for postoperative complications in oesophageal cancer
Ruben Duwaerts, Belgium
PO-2087
Toxicity modelling in RT using Bayesian network topology optimization with simulated annealing
Kailyn Stenhouse, Canada
PO-2089
Can Radiomics support Early Regression Index in predicting rectal cancer response to MRgRT?
Luca Boldrini, Italy
PO-2090
A method to explore dose and LET for normal tissue response studies in large proton therapy cohorts
Rasmus Klitgaard, Denmark
PO-2091
Early toxicity and diffusion-weighted MRI assessment after Single-Dose Ablative Radiotherapy for PCa
Denis Panizza, Italy
PO-2092
Deep-learning standardization of MR images in radiomic studies-Application to LACC T2w MRI
Stephane Niyoteka, France
PO-2093
Dosiomics applied to biological dose and LET maps to predict Local Recurrence in Sacral Chordoma
Chiara Paganelli, Italy
PO-2094
In silico identification of dose equivalences in hypofractionated prostate cancer radiotherapy
Renaud de Crevoisier, France
PO-2095
Machine learning prediction of Dice similarity coefficient for accuracy evaluation
Yun Ming Wong, Singapore
PO-2096
Bladder dose surface maps identify subregions associated to late toxicities after prostate cancer RT
Eliana Gioscio, Italy
PO-2099
Radiomic features and PFS post-PACIFIC in the Blue Sky Observational Study on stage 3 PDL1+ NSCLC.
Andrea R. Filippi, Italy
PO-2100
ADDED VALUE OF MRI RADIOMICS TO PREDICT PATHOLOGICAL STATUS OF PROSTATE CANCER PATIENTS
Maria Giulia Vincini, Italy
PO-2101
CT-based radiomics for outcome prediction in oropharyngeal cancer patients treated with curative RT
Stefania Volpe, Italy
PO-2102
Demonstrating variability in radiomic analysis due to inconsistent conversion from contour to mask.
Emiliano Spezi, United Kingdom
PO-2103
MRI-Only SBRT in Gliomas: Dosimetry, Biology, and Radiomics Evaluation of a Pseudo-CT Generation
Xin Yang, China
PO-2104
Impact of windowing CT scans on the performance of a CNN for head and neck cancer prognosis
Pedro Mateus, The Netherlands
PO-2105
Machine learning prediction of pain response to palliative radiation therapy with CT-based radiomics
Oscar Llorian, Germany
PO-2106
Challenges in international real world evidence research collaboration. The PREDMORN experience
Laia Humbert-Vidan, USA
PO-2107
Impact of breathing and image filtering on radiomic features derived from 4D-CT in early-STAGE NSCLC
Stefania Volpe, Italy
PO-2108
Impact of normalisation methods for longitudinal MR images on radiomic features.
Aaron Rankin, United Kingdom
PO-2109
Normalised odds ratios help understand the relative importance of dose in multivariable models
Emmy Dalqvist, Sweden
PO-2110
The role of combined radiomics features as predictor of response to CRT or BRT in patients with OPC
Haruo Inokuchi, Japan
PO-2111
Radiomics of diffusion-MRI for predicting Gleason Score in Prostate Cancer treated with radiotherapy
Chiara Paganelli, Italy
PO-2112
Machine learning and image-oriented methods for head and neck cancer treatment outcome prediction
Bao Ngoc Huynh, Norway
PO-2114
Manual or automatic heart contours do not lead to difference in predicted survival in NSCLC patients
Miguel Fernandes, The Netherlands
PO-2116
CTV-free lung planning: Can we reduce toxicity whilst maintaining local control?
Matthew Craddock, United Kingdom
PO-2117
Late toxicity predictors in high-risk prostate cancer radiotherapy using accumulated delivered dose
Ashley Ong, Singapore
PO-2118
Repeatability of Voxel-Based Analysis pipeline in radiation oncology: a first pilot experiment
Laura Cella, Italy
PO-2119
In silico simulation for better prediction of biochemical recurrence in prostate cancer radiotherapy
Renaud de Crevoisier, France
PO-2120
Effect of early fractional lymphocyte loss on lymphopenia probability models for NSCLC
Takahiro Kanehira, Japan
PO-2122
Multiparametric MRI and PET to predict ISUP grade groups in prostate cancer patients
Erik Nilsson, Sweden
PO-2124
Contrastive self-supervised learning of lung tumor imaging predicts immunotherapy response
Tafadzwa Lawrence Chaunzwa, USA
PO-2125
Integrative Radiomic-Clinical Model Improves Long-term Prognostication of High-risk Prostate Cancer
Chi Fung CHING, Hong Kong (SAR) China
PO-2126