Calderaro, Julien and Ghaffari Laleh, Narmin and Zeng, Qinghe and Maille, Pascale and Favre, Loetitia and Pujals, Anais and Klein, Christophe and Bazille, Celine and Heij, Lara R. and Uguen, Arnaud and Luedde, Tom and Di Tommaso, Luca and Beaufrere, Aurelie and Chatain, Augustin and Gastineau, Delphine and Nguyen, Cong Trung and Nguyen-Canh, Hiep and Thi, Khuyen Nguyen and Gnemmi, Viviane and Graham, Rondell P. and Charlotte, Frederic and Wendum, Dominique and Vij, Mukul and Allende, Daniela S. and Aucejo, Federico and Diaz, Alba and Riviere, Benjamin and Herrero, Astrid and Evert, Katja and Calvisi, Diego Francesco and Augustin, Jeremy and Leow, Wei Qiang and Leung, Howard Ho Wai and Boleslawski, Emmanuel and Rela, Mohamed and Francois, Arnaud and Cha, Anthony Wing-Hung and Forner, Alejandro and Reig, Maria and Allaire, Manon and Scatton, Olivier and Chatelain, Denis and Boulagnon-Rombi, Camille and Sturm, Nathalie and Menahem, Benjamin and Frouin, Eric and Tougeron, David and Tournigand, Christophe and Kempf, Emmanuelle and Kim, Haeryoung and Ningarhari, Massih and Michalak-Provost, Sophie and Gopal, Purva and Brustia, Raffaele and Vibert, Eric and Schulze, Kornelius and Ruether, Darius F. and Weidemann, Soeren A. and Rhaiem, Rami and Pawlotsky, Jean-Michel and Zhang, Xuchen and Luciani, Alain and Mule, Sebastien and Laurent, Alexis and Amaddeo, Giuliana and Regnault, Helene and De Martin, Eleonora and Sempoux, Christine and Navale, Pooja and Westerhoff, Maria and Lo, Regina Cheuk-Lam and Bednarsch, Jan and Gouw, Annette and Guettier, Catherine and Lequoy, Marie and Harada, Kenichi and Sripongpun, Pimsiri and Wetwittayaklang, Poowadon and Lomenie, Nicolas and Tantipisit, Jarukit and Kaewdech, Apichat and Shen, Jeanne and Paradis, Valerie and Caruso, Stefano and Kather, Jakob Nikolas (2023) Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma. NATURE COMMUNICATIONS, 14 (1): 8290. ISSN , 2041-1723
Full text not available from this repository. (Request a copy)Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | ARTIFICIAL-INTELLIGENCE; IMMUNE; |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Pathologie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 30 Jan 2024 06:40 |
| Last Modified: | 30 Jan 2024 06:40 |
| URI: | https://pred.uni-regensburg.de/id/eprint/61003 |
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