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Copenhagen, Denmark
Onsite/Online

ESTRO 2022

Session

Radiomics, modelling and statistical methods
7011
Poster (digital)
Physics
Machine Learning in NTCP prediction --- A superior alternative to the Lyman-Burman-Kutcher model
Pratik Samant, United Kingdom
PO-1755
Spatial Pyramid Pooling Survival Networks: Learning survival outcomes from whole slide images
Shenlun Chen, The Netherlands
PO-1756
A framework for in-vivo, voxel-based assessment of radiation response through multimodal imaging
Mikkel Skaarup, Denmark
PO-1757
Performance assessment of radiogenomics machine learning models for stratifying prostate cancer risk
Nerea Payan, United Kingdom
PO-1758
Delta Radiomics can predict complete pathological response in rectal cancer patients
Antonio Angrisani, Italy
PO-1759
Independent validation of a PET radiomic model predicting outcome after Radiotherapy for HN cancer
Martina Mori, Italy
PO-1760
Isotoxic temporal modulation of fraction size in conventional radiotherapy
Jan-Jakob Sonke, The Netherlands
PO-1761
Early detection of brain metastases using diffusion weighted imaging radiomics and machine learning
Joseph Madamesila, Canada
PO-1762
Radiomics and Deep Learning for the 2-Year Overall Survival Prediction in Lung Cancer
Anna Braghetto, Italy
PO-1763
Impact of dose errors on dose-response modelling
Louise Mövik, Sweden
PO-1764
Pre-processing and feature/volume correlation in CT radiomics in non-small cell lung cancer
Stefania Volpe, Italy
PO-1765
Deep learning classifier for MGMT promoter methylation status in glioblastoma cancer
Javier Barranco Garcia, Switzerland
PO-1766
Development of a MRI radiomic-based ML model to predict aggressiveness of prostate cancer
Odette Rios Ibacache, Chile
PO-1767
Regularized distributed Cox regression: a model for federated feature selection in survival analysis
BENEDETTA GOTTARDELLI, Italy
PO-1768
Prostate cancer radiogenomics machine learning classification for predicting disease progression
Ross Murphy, United Kingdom
PO-1769
Prediction of mandibular ORN with DL-based classification of 3D radiation dose distribution maps
Laia Humbert-Vidan, USA
PO-1770
Prediction of recurrence from post-operative MRI in GBM: Are we reaching limits of Deep-Learning?
Alexandre CARRÉ, France
PO-1771
Radiomic feature relevance in the prediction of pathological features of prostate cancer
Lars Johannes Isaksson, Italy
PO-1772
Feasibility of a novel harmonization method for NSCLC multi-centric radiomic studies
Andrea Botti, Italy
PO-1773
Oxygen distribution in the microenvironment and RT treatment outcome: a modeling study on metastasis
Luca Possenti, Italy
PO-1774
Associations of CT-based radiomics data with disease recurrence in early stage Lung cancer patients
Alexandra Giraldo Marin, Spain
PO-1775
Evaluation of two commercial deep learning OAR segmentation models for prostate cancer treatment
Jenny Gorgisyan, Sweden
PO-1776
Self-supervised image feature extraction for outcomes prediction in oropharyngeal cancer
Baoqiang Ma, The Netherlands
PO-1777
MRI Radiomics in prostate cancer: a reliability study
Antonio Angrisani, Italy
PO-1778
Detection of mandibular osteoradionecrosis using novel imaging biomarkers for head and neck cancer
Abdallah Mohamed, USA
PO-1779
Image-based data mining for radiation outcomes research applies to data from children
Abigail Bryce-Atkinson, United Kingdom
PO-1780
Radiomic features are minimally repeatable in test-retest MR images of cervical cancer
Chelmis Muthoni Thiong'o, United Kingdom
PO-1781
Methodological Quality of Machine Learning Quantitative Image Analysis Studies in Esophageal Cancer
Zhen Zhang, The Netherlands
PO-1782
Leverage radiomic and clinical data in predicting SRS treatment outcomes in patients with brain mets
Gianluca Carloni, Italy
PO-1783
predicting radiation pneumonitis based on retraining a deep learning feature extraction model
Zhixiang Wang, The Netherlands
PO-1784
Secondary cancer risk estimates from proton arc plans in pediatric craniopharyngioma.
Laura Toussaint, Denmark
PO-1785
Exploratory analysis of anatomical regions associated with Dysphagia measured with the MDADI
Antony Carver, United Kingdom
PO-1786
Impact of deep learning segmentation methods on the robustness of MR glioblastoma radiomics
Diem Vuong, Switzerland
PO-1787
Radiomic and dosiomic prediction of biochemical failure after Iodine-125 prostate brachytherapy
Masahiro Nakano, Japan
PO-1788
Quantitative evaluation of whole-body spatial normalisation in paediatric patients
Catarina Veiga, United Kingdom
PO-1789