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@ARTICLE{Galli:310363,
      author       = {R. Galli and S. Korn and D. Aust$^*$ and G. B. Baretton$^*$
                      and J. Weitz$^*$ and E. Koch and C. Riediger$^*$},
      title        = {{L}abel-free multiphoton microscopy and machine learning
                      for recognition of hepatocellular carcinoma.},
      journal      = {Scientific reports},
      volume       = {nn},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {DKFZ-2026-00559},
      pages        = {nn},
      year         = {2026},
      note         = {#NCTZFB9# / epub},
      abstract     = {Complete tumor resection is crucial in oncological liver
                      surgery, and the evaluation of intraoperative resection
                      margins is essential to prove R0 resection. This can be
                      challenging for hepatocellular carcinoma (HCC) due to the
                      heterogeneity of both the tumor and background liver tissue.
                      Label-free multiphoton microscopy (MPM) enables tissue
                      analysis based on endogenous optical signals, and has the
                      potential for intraoperative real-time assessment of
                      resection planes. Matched samples of human HCC and
                      background liver tissue from 76 patients were imaged using a
                      multimodal approach, including coherent anti-Stokes Raman
                      scattering, two-photon autofluorescence, and second harmonic
                      generation. The morphological information contained in each
                      channel was reduced to 17 texture parameters that were used
                      for classification. A neural network model was trained on
                      approximately 25,000 images (35 patients) and used to
                      classify a test set of approximately 27,000 images (38
                      patients) as well as create maps showing the tumor border (3
                      patients). Label-free MPM revealed HCC growth patterns as
                      well as steatotic and desmoplastic features. Accurate tumor
                      recognition was achieved on low-lateral-resolution MPM
                      images, mimicking the use of endoscopes. The model achieved
                      a test set correct rate of $97.3\%$ $(98.2\%$ for liver and
                      $96.5\%$ for tumor). Analysis of the contribution of the
                      different nonlinear signals to the classification showed
                      that autofluorescence plays a key role in discriminating
                      between neoplastic and non-neoplastic tissue. In conclusion,
                      label-free intraoperative optical histopathology of HCC has
                      the potential to improve tumor resection margins. By
                      implementation in endoscopes, MPM may enable on-site tissue
                      analysis for optimization of tumor identification or
                      characterization of liver tissue.},
      keywords     = {Autofluorescence (Other) / Coherent anti-stokes Raman
                      scattering (Other) / Oncological surgery (Other) / Second
                      harmonic generation (Other) / Texture analysis (Other)},
      cin          = {DD04},
      ddc          = {600},
      cid          = {I:(DE-He78)DD04-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41803471},
      doi          = {10.1038/s41598-026-43831-y},
      url          = {https://inrepo02.dkfz.de/record/310363},
}