Dataset Open Access
Data publication: A deep-learning-based surrogate model for Monte-Carlo simulations of the linear energy transfer in primary brain tumor patients treated with proton-beam radiotherapy
Starke, Sebastian;
Kieslich, Aaron Markus;
Palkowitsch, Martina;
Hennings, Fabian;
Troost, Esther Gera Cornelia;
Krause, Mechthild;
Bensberg, Jona;
Hahn, Christian;
Heinzelmann, Feline;
Bäumer, Christian;
Lühr, Armin;
Timmermann, Beate;
Löck, Steffen
BibTeX Export
@misc{starke_sebastian_2024_2764,
author = {Starke, Sebastian and
Kieslich, Aaron Markus and
Palkowitsch, Martina and
Hennings, Fabian and
Troost, Esther Gera Cornelia and
Krause, Mechthild and
Bensberg, Jona and
Hahn, Christian and
Heinzelmann, Feline and
Bäumer, Christian and
Lühr, Armin and
Timmermann, Beate and
Löck, Steffen},
title = {{Data publication: A deep-learning-based surrogate
model for Monte-Carlo simulations of the linear
energy transfer in primary brain tumor patients
treated with proton-beam radiotherapy}},
month = mar,
year = 2024,
doi = {10.14278/rodare.2764},
url = {https://doi.org/10.14278/rodare.2764}
}
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| All versions | This version | |
|---|---|---|
| Views | 1,518 | 823 |
| Downloads | 54 | 34 |
| Data volume | 10.0 GB | 7.7 GB |
| Unique views | 1,205 | 677 |
| Unique downloads | 48 | 30 |
- Publication date:
- March 15, 2024
- DOI:
-
- Keyword(s):
- proton-beam therapy relative biological effectiveness linear energy transfer NTCP models deep learning brain tumor
- Related identifiers:
- Identical to:
https://www.hzdr.de/publications/Publ-38860 - Referenced by:
https://www.hzdr.de/publications/Publ-38858 - Communities:
- License (for files):
- Creative Commons Attribution 4.0 International