2026-04-21T01:06:46Z
https://rodare.hzdr.de/oai2d
oai:rodare.hzdr.de:4185
2025-12-19T13:30:26Z
openaire_data
user-health
user-rodare
user-oncoray
true
3.1
HZDR.RODARE
10.14278/rodare.4185
Kieslich, Aaron Markus
0009-0000-2227-2013
Singh, Yerik
Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-Universität München, München, Germany
Palkowitsch, Martina
0009-0009-7420-208X
Starke, Sebastian
0000-0001-5007-1868
Hennings, Fabian
0000-0003-3988-4272
Troost, Esther Gera Cornelia
0000-0001-9550-9050
Krause, Mechthild
0000-0003-1776-9556
Bensberg, Jona
0000-0003-0851-9176
TU Dortmund University, Department of Physics, Dortmund, Germany
Lühr, Armin
0000-0002-9450-6859
TU Dortmund University, Department of Physics, Dortmund, Germany
Heinzelmann, Feline
West German Proton Therapy Center Essen (WPE), University Hospital Essen, Essen, Germany
Bäumer, Christian
0000-0003-0512-1146
TU Dortmund University, Department of Physics, Dortmund, Germany
Timmermann, Beate
0000-0003-2274-2008
West German Proton Therapy Center Essen (WPE), University Hospital Essen, Essen, Germany
Depauw, Nicolas
0000-0002-0486-6149
Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
Shih, Helen A.
0000-0003-1550-9726
Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
Löck, Steffen
0000-0002-7017-3738
Data publication: Deep learning for dose-averaged linear energy transfer estimation in pencil-beam scanning and double scattering proton plans with uncertainty-aware external validation
Rodare
2025
proton radiotherapy
linear energy transfer
deep learning
pencil beam scanning
double scattering
uncertainty quantification
2025-12-16
https://rodare.hzdr.de/record/4185
https://www.hzdr.de/publications/Publ-42451
https://www.hzdr.de/publications/Publ-42419
10.14278/rodare.4184
https://rodare.hzdr.de/communities/health
https://rodare.hzdr.de/communities/oncoray
https://rodare.hzdr.de/communities/rodare
Version 1.0.0
Creative Commons Attribution 4.0 International
Open Access
<p>This repository contains the outputs, model checkpoints and result data of our deep-learning-based experiments for the approximation of Monte-Carlo-simulated linear energy transfer distributions and uncertainty estimation, which build the foundation for the corresponding article.</p>