; Uguen, Arnaud ; Luedde, Tom
; Di Tommaso, Luca ; Beaufrère, Aurélie ; Chatain, Augustin ; Gastineau, Delphine ; Nguyen, Cong Trung ; Nguyen-Canh, Hiep ; Thi, Khuyen Nguyen ; Gnemmi, Viviane ; Graham, Rondell P. ; Charlotte, Frédéric ; Wendum, Dominique ; Vij, Mukul ; Allende, Daniela S. ; Aucejo, Federico ; Diaz, Alba ; Rivière, Benjamin ; Herrero, Astrid ; Evert, Katja ; Calvisi, Diego Francesco ; Augustin, Jérémy ; Leow, Wei Qiang ; Leung, Howard Ho Wai ; Boleslawski, Emmanuel ; Rela, Mohamed ; François, Arnaud ; Cha, Anthony Wing-Hung ; Forner, Alejandro ; Reig, Maria ; Allaire, Manon ; Scatton, Olivier ; Chatelain, Denis ; Boulagnon-Rombi, Camille ; Sturm, Nathalie ; Menahem, Benjamin ; Frouin, Eric ; Tougeron, David ; Tournigand, Christophe ; Kempf, Emmanuelle ; Kim, Haeryoung ; Ningarhari, Massih ; Michalak-Provost, Sophie ; Gopal, Purva ; Brustia, Raffaele ; Vibert, Eric ; Schulze, Kornelius ; Rüther, Darius F.
; Weidemann, Sören A. ; Rhaiem, Rami ; Pawlotsky, Jean-Michel ; Zhang, Xuchen ; Luciani, Alain ; Mulé, Sébastien ; Laurent, Alexis ; Amaddeo, Giuliana ; Regnault, Hélène ; De Martin, Eleonora ; Sempoux, Christine ; Navale, Pooja ; Westerhoff, Maria ; Lo, Regina Cheuk-Lam ; Bednarsch, Jan ; Gouw, Annette ; Guettier, Catherine ; Lequoy, Marie ; Harada, Kenichi ; Sripongpun, Pimsiri ; Wetwittayaklang, Poowadon ; Loménie, Nicolas ; Tantipisit, Jarukit ; Kaewdech, Apichat ; Shen, Jeanne ; Paradis, Valérie ; Caruso, Stefano ; Kather, Jakob Nikolas | Dokumentenart: | Artikel | ||||
|---|---|---|---|---|---|
| Titel eines Journals oder einer Zeitschrift: | Nature Communications | ||||
| Verlag: | NATURE PORTFOLIO | ||||
| Ort der Veröffentlichung: | BERLIN | ||||
| Band: | 14 | ||||
| Nummer des Zeitschriftenheftes oder des Kapitels: | 1 | ||||
| Datum: | 2023 | ||||
| Institutionen: | Medizin > Lehrstuhl für Pathologie | ||||
| Identifikationsnummer: |
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| Stichwörter / Keywords: | ARTIFICIAL-INTELLIGENCE; IMMUNE; | ||||
| Dewey-Dezimal-Klassifikation: | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin | ||||
| Status: | Veröffentlicht | ||||
| Begutachtet: | Ja, diese Version wurde begutachtet | ||||
| An der Universität Regensburg entstanden: | Ja | ||||
| Dokumenten-ID: | 76314 |

Zusammenfassung
Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep ...

Zusammenfassung
Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical outcomes, genetic alterations and in situ spatial gene expression profiling. This type of approach could improve treatment decisions and ultimately clinical outcome for patients with rare and biphenotypic cancers such as cHCC-CCA. Combined hepatocellular-cholangiocarcinomas (cHCC-CCA) are challenging to diagnose, as they exhibit features of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICCA). Here, the authors use deep learning to re-classify cHCC-CCA tumours into HCC or ICCA based on histopathology images.
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