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
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<identifier identifierType="DOI">10.14278/rodare.2764</identifier>
<creators>
<creator>
<creatorName>Starke, Sebastian</creatorName>
<givenName>Sebastian</givenName>
<familyName>Starke</familyName>
<nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5007-1868</nameIdentifier>
</creator>
<creator>
<creatorName>Kieslich, Aaron Markus</creatorName>
<givenName>Aaron Markus</givenName>
<familyName>Kieslich</familyName>
</creator>
<creator>
<creatorName>Palkowitsch, Martina</creatorName>
<givenName>Martina</givenName>
<familyName>Palkowitsch</familyName>
</creator>
<creator>
<creatorName>Hennings, Fabian</creatorName>
<givenName>Fabian</givenName>
<familyName>Hennings</familyName>
</creator>
<creator>
<creatorName>Troost, Esther Gera Cornelia</creatorName>
<givenName>Esther Gera Cornelia</givenName>
<familyName>Troost</familyName>
<nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-9550-9050</nameIdentifier>
</creator>
<creator>
<creatorName>Krause, Mechthild</creatorName>
<givenName>Mechthild</givenName>
<familyName>Krause</familyName>
<nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-1776-9556</nameIdentifier>
</creator>
<creator>
<creatorName>Bensberg, Jona</creatorName>
<givenName>Jona</givenName>
<familyName>Bensberg</familyName>
</creator>
<creator>
<creatorName>Hahn, Christian</creatorName>
<givenName>Christian</givenName>
<familyName>Hahn</familyName>
</creator>
<creator>
<creatorName>Heinzelmann, Feline</creatorName>
<givenName>Feline</givenName>
<familyName>Heinzelmann</familyName>
</creator>
<creator>
<creatorName>Bäumer, Christian</creatorName>
<givenName>Christian</givenName>
<familyName>Bäumer</familyName>
</creator>
<creator>
<creatorName>Lühr, Armin</creatorName>
<givenName>Armin</givenName>
<familyName>Lühr</familyName>
<nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-9450-6859</nameIdentifier>
</creator>
<creator>
<creatorName>Timmermann, Beate</creatorName>
<givenName>Beate</givenName>
<familyName>Timmermann</familyName>
</creator>
<creator>
<creatorName>Löck, Steffen</creatorName>
<givenName>Steffen</givenName>
<familyName>Löck</familyName>
<nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-7017-3738</nameIdentifier>
</creator>
</creators>
<titles>
<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</title>
</titles>
<publisher>Rodare</publisher>
<publicationYear>2024</publicationYear>
<subjects>
<subject>proton-beam therapy</subject>
<subject>relative biological effectiveness</subject>
<subject>linear energy transfer</subject>
<subject>NTCP models</subject>
<subject>deep learning</subject>
<subject>brain tumor</subject>
</subjects>
<dates>
<date dateType="Issued">2024-03-15</date>
</dates>
<resourceType resourceTypeGeneral="Dataset"/>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://rodare.hzdr.de/record/2764</alternateIdentifier>
</alternateIdentifiers>
<relatedIdentifiers>
<relatedIdentifier relatedIdentifierType="URL" relationType="IsIdenticalTo">https://www.hzdr.de/publications/Publ-38860</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="URL" relationType="IsReferencedBy">https://www.hzdr.de/publications/Publ-38858</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.14278/rodare.2763</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/oncoray</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/rodare</relatedIdentifier>
</relatedIdentifiers>
<rightsList>
<rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
</rightsList>
<descriptions>
<description descriptionType="Abstract"><p>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.</p>
<p>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.</p>
<p>&nbsp;</p>
<p>Moreover, we provide an exemplary data sample from a water phantom for trying our analysis pipeline.</p></description>
</descriptions>
</resource>
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