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@ARTICLE{Schallenberg:305621,
      author       = {S. Schallenberg and G. Dernbach and S. Ruane and P.
                      Jurmeister and C. Böhm and K. Standvoss and S. Ghosh and M.
                      Frentsch and M.-P. Dragomir$^*$ and P. G. Keyl and C.
                      Friedrich and I.-K. Na$^*$ and S. Merkelbach-Bruse and A.
                      Quaas and N. Frost and K. Boschung and W. Randerath and G.
                      Schlachtenberger and M. Heldwein and U. Keilholz and K.
                      Hekmat and J.-C. Rückert and R. Büttner and A. Vasaturo
                      and D. Horst and L. Ruff and M. Alber and K.-R. Müller and
                      F. Klauschen$^*$},
      title        = {{AI}-powered spatial cell phenomics enhances risk
                      stratification in non-small cell lung cancer.},
      journal      = {Nature Communications},
      volume       = {16},
      number       = {1},
      issn         = {2041-1723},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {DKFZ-2025-02269},
      pages        = {9701},
      year         = {2025},
      abstract     = {Risk stratification remains a critical challenge in
                      non-small cell lung cancer patients for optimal therapy
                      selection. In this study, we develop an artificial
                      intelligence-powered spatial cellomics approach that
                      combines histology, multiplex immunofluorescence imaging and
                      multimodal machine learning to characterize the complex
                      cellular relationships of 43 cell phenotypes in the tumor
                      microenvironment in a real-world retrospective cohort of
                      1168 non-small cell lung cancer patients from two large
                      German cancer centers. The model identifies cell niches
                      associated with survival and achieves a $14\%$ and $47\%$
                      improvement in risk stratification in the two main non-small
                      cell lung cancer subtypes, lung adenocarcinoma and squamous
                      cell carcinoma, respectively, combining niche patterns with
                      conventional cancer staging. Our results show that complex
                      immune cell niche patterns identify potentially undertreated
                      high-risk patients qualifying for adjuvant therapy. Our
                      approach highlights the potential of artificial intelligence
                      powered multiplex imaging analyses to better understand the
                      contribution of the tumor microenvironment to cancer
                      progression and to improve risk stratification and treatment
                      selection in non-small cell lung cancer.},
      keywords     = {Humans / Carcinoma, Non-Small-Cell Lung: pathology /
                      Carcinoma, Non-Small-Cell Lung: mortality / Carcinoma,
                      Non-Small-Cell Lung: diagnostic imaging / Lung Neoplasms:
                      pathology / Lung Neoplasms: mortality / Tumor
                      Microenvironment / Female / Male / Artificial Intelligence /
                      Retrospective Studies / Phenomics: methods / Middle Aged /
                      Aged / Risk Assessment: methods / Machine Learning /
                      Carcinoma, Squamous Cell: pathology},
      cin          = {BE01 / MU01},
      ddc          = {500},
      cid          = {I:(DE-He78)BE01-20160331 / I:(DE-He78)MU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41184299},
      doi          = {10.1038/s41467-025-65783-z},
      url          = {https://inrepo02.dkfz.de/record/305621},
}