% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{RabascoMeneghetti:309874,
      author       = {A. Rabasco Meneghetti$^*$ and C. Campani and C. Roux and Z.
                      I. Carrero and D.-A. Popica and G. Amaddeo and M. Lequoy and
                      C. Hollande and S. Mouri and M. Wagner and V. Plaforet and
                      S. Sidali and M. Ronot and M. Rudler and A. Luciani and O.
                      Sutter and E. Spitzer and H. Regnault and S. El Mouhadi and
                      V. Ozenne and N. Ganne-Carrié and M. Bouattour and J. C.
                      Nault and D. Thabut and J. N. Kather$^*$ and M. Allaire},
      title        = {{D}etection of {E}sophageal {V}arices and {P}rediction of
                      {H}epatic {D}ecompensation in {U}nresectable
                      {H}epatocellular {C}arcinoma using {AI}: {AI} {D}etection of
                      {V}arices and {D}ecompensation.},
      journal      = {Journal of hepatology},
      volume       = {nn},
      issn         = {0168-8278},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DKFZ-2026-00359},
      pages        = {nn},
      year         = {2026},
      note         = {#DKTKZFB9# / #NCTZFB9# / epub},
      abstract     = {In hepatocellular carcinoma (HCC) with cirrhosis, portal
                      hypertension (PHT) worsens outcomes.
                      Esophagogastroduodenoscopy (EGD), current screen for
                      esophageal varices (EVs), is invasive and delays therapy. We
                      aimed to develop and externally validate non-invasive models
                      to detect EV and predict hepatic decompensation (bleeding,
                      ascites or hepatic encephalopathy), a major cause of HCC
                      mortality, using routine contrast-enhanced CT (CECT) and
                      clinical data.This multicenter retrospective study included
                      489 patients with unresectable HCC treated with
                      Atezolizumab-Bevacizumab (AtezoBev) from five French
                      centers, split into a development (n=279) and external
                      validation (n=210) cohorts. Arterial-phase CECTs were
                      processed through a Deep Learning pipeline using a
                      foundation model (HepatoSageCT). Logistic and Cox models
                      generated clinical models and combined models integrating
                      the HepatoSageCT scores with key clinical variables for EVs
                      and hepatic decompensation. Performance was assessed using
                      AUCROC, sensitivity, specificity, concordance index and
                      cause-specific hazard ratio.Portosystemic shunts (PSS) at
                      imaging identified EVs with AUCROC of 0.78, increasing to
                      0.84 when combined with HepatoSageCT. A decision algorithm
                      incorporating PSS and HepatoSageCT missed $4.2\%$ of varices
                      needing treatment, compared to $8.4\%$ when using only PSS
                      while missing $0\%$ of large EVs. HepatoSageCT predicted
                      hepatic decompensation in validation (C-Index: 0.73, hazard
                      ratio: 3.17) with significant stratification (p<0.001)
                      comparable to a score of ascites, splenomegaly and
                      HepatoSageCT risk (C-Index: 0.73, hazard ratio: 3.48).
                      Patients stratified at higher risk of decompensation by
                      HepatoSageCT also showed significantly (p<0.001) lower
                      overall survival (OS).HepatoSageCT scores, supplemented with
                      clinical data, enables accurate non-invasive detection of EV
                      in AtezoBev-treated unresectable HCC and stratifying
                      patients based on hepatic decompensation risk, potentially
                      assisting in reducing unnecessary endoscopies and guiding
                      prognosis.The present study demonstrates that foundation
                      models applied to routine CT imaging, when combined with
                      routinely-collected features such as the presence of
                      portosystemic shunts, can accurately predict the presence of
                      esophageal varices and risk of hepatic and first hepatic
                      decompensation in patients with AtezoBev-treated
                      unresectable HCC. These findings are particularly relevant
                      for hepatologists and oncologists, as they highlight a
                      promising non-invasive tool for timely risk assessment in a
                      time-sensitive patient population. While prospective
                      validation is warranted, this approach could support more
                      personalized management and care of patients with
                      unresectable HCC.},
      keywords     = {deep learning (Other) / hepatic decompensation (Other) /
                      hepatocellular carcinoma (Other) / portal hypertension
                      (Other) / portosystemic shunt (Other) / varices (Other)},
      cin          = {DD01 / HD02},
      ddc          = {610},
      cid          = {I:(DE-He78)DD01-20160331 / I:(DE-He78)HD02-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41679555},
      doi          = {10.1016/j.jhep.2026.01.021},
      url          = {https://inrepo02.dkfz.de/record/309874},
}