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@ARTICLE{Rose:304478,
      author       = {F. Rose and O. Ibruli and L. Lichius and M. Kiljan and G.
                      Gozum and M. I. Caiaffa and J. Cai and L.-N. Niu and J. M.
                      Herter and H. Grüll and R. Büttner and F. Beleggia and G.
                      Bosco and J. George and G. S. Herter-Sprie and H. C.
                      Reinhardt$^*$ and K. Bozek},
      title        = {{I}maging mass cytometry dataset of small-cell lung cancer
                      tumors and tumor microenvironments.},
      journal      = {BMC Research Notes},
      volume       = {18},
      number       = {1},
      issn         = {1756-0500},
      address      = {London},
      publisher    = {[Verlag nicht ermittelbar]},
      reportid     = {DKFZ-2025-01870},
      pages        = {385},
      year         = {2025},
      abstract     = {Small cell lung cancer (SCLC) accounts for approximately
                      $15\%$ of lung tumors and is marked by aggressive growth and
                      early metastatic spread. In this study, we used two SCLC
                      mouse models with differing tumor mutation burdens (TMB). To
                      investigate tumor composition, spatial architecture, and
                      interactions with the surrounding microenvironment, we
                      acquired multiplexed images of mouse lung tumors using
                      imaging mass cytometry (IMC). These data build upon a
                      previously published characterization of the mouse
                      model.After tumor detection, mice were assigned to one of
                      five treatment groups. Lung tumor tissues were imaged with a
                      37-marker IMC panel designed to identify major cell
                      types-tumor, immune, and structural-as well as their
                      functional states. When possible, each tumor was sampled
                      both at its center and border regions. Tumor masks in the
                      form of binary images are provided to delineate tumor areas.
                      Additional metadata include tumor onset and endpoint dates
                      to support downstream correlation or predictive analyses
                      based on the image data. This dataset offers a valuable
                      resource for studying the histological and cellular
                      complexity of SCLC in a genetically controlled mouse model
                      across multiple therapeutic conditions.},
      keywords     = {Animals / Tumor Microenvironment / Small Cell Lung
                      Carcinoma: pathology / Small Cell Lung Carcinoma: diagnostic
                      imaging / Small Cell Lung Carcinoma: genetics / Lung
                      Neoplasms: pathology / Lung Neoplasms: diagnostic imaging /
                      Lung Neoplasms: genetics / Mice / Disease Models, Animal /
                      Image Cytometry: methods / Hyperion (Other) / IMC (Other) /
                      MIBI-TOF (Other) / Mouse models (Other) / SCLC (Other) /
                      Tumor microenvironment (Other)},
      cin          = {ED01},
      ddc          = {570},
      cid          = {I:(DE-He78)ED01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40922010},
      pmc          = {pmc:PMC12418687},
      doi          = {10.1186/s13104-025-07460-4},
      url          = {https://inrepo02.dkfz.de/record/304478},
}