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

MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nmm##2200000uu#4500</leader>
  <controlfield tag="001">2764</controlfield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Kieslich, Aaron Markus</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Palkowitsch, Martina</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Hennings, Fabian</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Troost, Esther Gera Cornelia</subfield>
    <subfield code="0">(orcid)0000-0001-9550-9050</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Krause, Mechthild</subfield>
    <subfield code="0">(orcid)0000-0003-1776-9556</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Bensberg, Jona</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Hahn, Christian</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Heinzelmann, Feline</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Bäumer, Christian</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Lühr, Armin</subfield>
    <subfield code="0">(orcid)0000-0002-9450-6859</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Timmermann, Beate</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Löck, Steffen</subfield>
    <subfield code="0">(orcid)0000-0002-7017-3738</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">proton-beam therapy</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">relative biological effectiveness</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">linear energy transfer</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">NTCP models</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">deep learning</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">brain tumor</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&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;</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="o">oai:rodare.hzdr.de:2764</subfield>
    <subfield code="p">openaire_data</subfield>
    <subfield code="p">user-rodare</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.14278/rodare.2764</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="a">Starke, Sebastian</subfield>
    <subfield code="0">(orcid)0000-0001-5007-1868</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">dataset</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-rodare</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">342692235</subfield>
    <subfield code="u">https://rodare.hzdr.de/record/2764/files/analysis_data.zip</subfield>
    <subfield code="z">md5:d0b670f5cee1de6f117919f2e83d92f9</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">93715492</subfield>
    <subfield code="u">https://rodare.hzdr.de/record/2764/files/water_phantom.zip</subfield>
    <subfield code="z">md5:75ae4c12df1ec734429e041cf0d50182</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="a">https://www.hzdr.de/publications/Publ-38860</subfield>
    <subfield code="i">isIdenticalTo</subfield>
    <subfield code="n">url</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="a">https://www.hzdr.de/publications/Publ-38858</subfield>
    <subfield code="i">isReferencedBy</subfield>
    <subfield code="n">url</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="a">10.14278/rodare.2763</subfield>
    <subfield code="i">isVersionOf</subfield>
    <subfield code="n">doi</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">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</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2024-03-15</subfield>
  </datafield>
  <controlfield tag="005">20240425061817.0</controlfield>
</record>
225
15
views
downloads
All versions This version
Views 225225
Downloads 1515
Data volume 3.4 GB3.4 GB
Unique views 210210
Unique downloads 1313
More info on how stats are collected.
Publication date:
March 15, 2024
DOI:
10.14278/rodare.2764

RODARE DOI Badge

DOI 

10.14278/rodare.2764

Markdown 

[![DOI](https://rodare.hzdr.de/badge/DOI/10.14278/rodare.2764.svg)](https://doi.org/10.14278/rodare.2764)

reStructedText 

.. image:: https://rodare.hzdr.de/badge/DOI/10.14278/rodare.2764.svg
   :target: https://doi.org/10.14278/rodare.2764

HTML 

<a href="https://doi.org/10.14278/rodare.2764"><img src="https://rodare.hzdr.de/badge/DOI/10.14278/rodare.2764.svg" alt="DOI"></a>

Image URL 

https://rodare.hzdr.de/badge/DOI/10.14278/rodare.2764.svg

Target URL 

https://doi.org/10.14278/rodare.2764

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

Versions

Version 1 10.14278/rodare.2764 Mar 15, 2024
Cite all versions? You can cite all versions by using the DOI 10.14278/rodare.2763. This DOI represents all versions, and will always resolve to the latest one. Read more.

Share

Cite as

Export