March 15, 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

JSON-LD (schema.org) Export

{
  "distribution": [
    {
      "contentUrl": "https://rodare.hzdr.de/api/files/cb0665ed-43e9-4f45-9c28-dcc3c860f3ac/analysis_data.zip", 
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      "@type": "DataDownload"
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  "url": "https://rodare.hzdr.de/record/2764", 
  "@type": "Dataset", 
  "@context": "https://schema.org/", 
  "creator": [
    {
      "name": "Starke, Sebastian", 
      "@type": "Person", 
      "@id": "https://orcid.org/0000-0001-5007-1868"
    }, 
    {
      "name": "Kieslich, Aaron Markus", 
      "@type": "Person"
    }, 
    {
      "name": "Palkowitsch, Martina", 
      "@type": "Person"
    }, 
    {
      "name": "Hennings, Fabian", 
      "@type": "Person"
    }, 
    {
      "name": "Troost, Esther Gera Cornelia", 
      "@type": "Person", 
      "@id": "https://orcid.org/0000-0001-9550-9050"
    }, 
    {
      "name": "Krause, Mechthild", 
      "@type": "Person", 
      "@id": "https://orcid.org/0000-0003-1776-9556"
    }, 
    {
      "name": "Bensberg, Jona", 
      "@type": "Person"
    }, 
    {
      "name": "Hahn, Christian", 
      "@type": "Person"
    }, 
    {
      "name": "Heinzelmann, Feline", 
      "@type": "Person"
    }, 
    {
      "name": "B\u00e4umer, Christian", 
      "@type": "Person"
    }, 
    {
      "name": "L\u00fchr, Armin", 
      "@type": "Person", 
      "@id": "https://orcid.org/0000-0002-9450-6859"
    }, 
    {
      "name": "Timmermann, Beate", 
      "@type": "Person"
    }, 
    {
      "name": "L\u00f6ck, Steffen", 
      "@type": "Person", 
      "@id": "https://orcid.org/0000-0002-7017-3738"
    }
  ], 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "description": "<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>\n\n<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>\n\n<p>&nbsp;</p>\n\n<p>Moreover, we provide an exemplary data sample from a water phantom for trying our analysis pipeline.</p>", 
  "identifier": "https://doi.org/10.14278/rodare.2764", 
  "sameAs": [
    "https://www.hzdr.de/publications/Publ-38860"
  ], 
  "name": "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", 
  "keywords": [
    "proton-beam therapy", 
    "relative biological effectiveness", 
    "linear energy transfer", 
    "NTCP models", 
    "deep learning", 
    "brain tumor"
  ], 
  "@id": "https://doi.org/10.14278/rodare.2764", 
  "datePublished": "2024-03-15"
}