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@ARTICLE{Mller:310362,
      author       = {L. Müller and J. N. Kather$^*$ and J. U. Marquardt and M.
                      Reig and Q. Wang and D. Pinto Dos Santos and R. Kloeckner},
      title        = {{BCLC} classification and {AI}-based image quantification:
                      {W}hat is meant to be will come together - but how and
                      when?: {BCLC} and {AI}-based image quantification.},
      journal      = {Journal of hepatology},
      volume       = {nn},
      issn         = {0168-8278},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DKFZ-2026-00558},
      pages        = {nn},
      year         = {2026},
      note         = {#NCTZFB9# / epub},
      abstract     = {The Barcelona Clinic Liver Cancer (BCLC) classification has
                      been the mainstay for prognostic assessment and initial
                      treatment selection in hepatocellular carcinoma (HCC) for
                      more than two decades. It is widely clinically accepted and
                      has been reaffirmed in the recently renewed European
                      Association for the Study of the Liver (EASL) Clinical
                      Practice Guidelines on the management of HCC. Its design is
                      based on simple clinical and imaging parameters, which makes
                      it highly applicable in clinical routine. However, it does
                      not fully utilize all information, which is potentially
                      encoded in routine radiology imaging. With artificial
                      intelligence (AI) methods now maturing, we have a robust way
                      to extract and quantify digital imaging features fully
                      automatically without much user input and with high
                      precision. Therefore, AI could bridge quantitative imaging
                      into clinical decision-making, together with the existing
                      BCLC classification. However, despite substantial AI
                      advancements in many fields such as automated tumor
                      volumetry, radiomics, detection of metastatic lesions, and
                      even capturing opportunistic imaging biomarkers, a
                      translational gap persists. While challenges related to
                      technical, administrative, and cost-related, but also
                      training-related factors have to be taken into account, a
                      certain aversion to change, as well as absence of
                      standardized AI validation and missing workflow integration
                      hamper the clinical implementation in routine care. This
                      article aims to evaluate current AI-quantified imaging
                      parameters and their potential for synergy with the
                      established BCLC classification.},
      keywords     = {Artificial intelligence (Other) / Classification (Other) /
                      Hepatocellular Carcinoma (Other) / Prognosis (Other) /
                      Quantification (Other)},
      cin          = {HD02},
      ddc          = {610},
      cid          = {I:(DE-He78)HD02-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41794137},
      doi          = {10.1016/j.jhep.2026.02.027},
      url          = {https://inrepo02.dkfz.de/record/310362},
}