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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

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    <dcat:keyword>relative biological effectiveness</dcat:keyword>
    <dcat:keyword>linear energy transfer</dcat:keyword>
    <dcat:keyword>NTCP models</dcat:keyword>
    <dcat:keyword>deep learning</dcat:keyword>
    <dcat:keyword>brain tumor</dcat:keyword>
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    <dct:description>&lt;p&gt;This repository contains the outputs and result data of our deep-learning-based experiments for the approximation of Monte-Carlo-simulated linear energy transfer distributions, which build the foundation for the corresponding article.&lt;/p&gt; &lt;p&gt;The Pytorch checkpoint of our finally chosen SegResNet architecture trained on the UPTD dose distributions is located at dd_pbs/Dose-LETd/clip_let_below_0.04/segresnet/all_trainvalid_data/training/lightning_logs/version_6358843/checkpoints/last.ckpt.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;Moreover, we provide an exemplary data sample from a water phantom for trying our analysis pipeline.&lt;/p&gt;</dct:description>
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Publication date:
March 15, 2024
DOI:
10.14278/rodare.2764

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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
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License (for files):
Creative Commons Attribution 4.0 International

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Version 2 10.14278/rodare.3025 Jun 21, 2024
Version 1 10.14278/rodare.2764 Mar 15, 2024
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