June 21, 2024 Dataset Open Access

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

Dublin Core Export

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  <dc:creator>Starke, Sebastian</dc:creator>
  <dc:creator>Kieslich, Aaron Markus</dc:creator>
  <dc:creator>Palkowitsch, Martina</dc:creator>
  <dc:creator>Hennings, Fabian</dc:creator>
  <dc:creator>Troost, Esther Gera Cornelia</dc:creator>
  <dc:creator>Krause, Mechthild</dc:creator>
  <dc:creator>Bensberg, Jona</dc:creator>
  <dc:creator>Hahn, Christian</dc:creator>
  <dc:creator>Heinzelmann, Feline</dc:creator>
  <dc:creator>Bäumer, Christian</dc:creator>
  <dc:creator>Lühr, Armin</dc:creator>
  <dc:creator>Timmermann, Beate</dc:creator>
  <dc:creator>Löck, Steffen</dc:creator>
  <dc:date>2024-06-21</dc:date>
  <dc:description>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.

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.

Moreover, we provide an exemplary data sample from a water phantom for trying our analysis pipeline.

Update: 

In this new version we added results of the gamma analyses and the results obtained when trained on the same data as the above model with the difference that we did not clip Monte-Carlo-simulated LET maps as requested during the review process.</dc:description>
  <dc:identifier>https://rodare.hzdr.de/record/3025</dc:identifier>
  <dc:identifier>10.14278/rodare.3025</dc:identifier>
  <dc:identifier>oai:rodare.hzdr.de:3025</dc:identifier>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-38860</dc:relation>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-39339</dc:relation>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-38858</dc:relation>
  <dc:relation>doi:10.14278/rodare.2763</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/oncoray</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/rodare</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>proton-beam therapy</dc:subject>
  <dc:subject>relative biological effectiveness</dc:subject>
  <dc:subject>linear energy transfer</dc:subject>
  <dc:subject>NTCP models</dc:subject>
  <dc:subject>deep learning</dc:subject>
  <dc:subject>brain tumor</dc:subject>
  <dc: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</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>