Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
- Université Paris-Est Créteil
- Hôpital Henri Mondor
- École nationale vétérinaire d'Alfort
- European Reference Network on Rare Hepatological Diseases
- Centre de Recherche des Cordeliers (CRC)
- Laboratoire d’Informatique Paris Descartes
- Sorbonne Université
- Université de Caen
- CHU de Brest
- Université de Bretagne Occidentale
- University Hospital Duesseldorf
- Humanitas University
- Hopital Beaujon
- Centre de Recherche sur l'Inflammation
- Hanoi Medical University
- Bach Mai Hospital
- Kanazawa University
- Vietnam National Cancer Hospital
- Université de Lille
- Mayo Clinic Rochester, MN
- Bharath Institute of Higher Education and Research
- Cleveland Clinic Foundation
- University of Barcelona
- CHU Montpellier
- University of Regensburg
- Singapore General Hospital
- Chinese University of Hong Kong
- CHU Hôpitaux de Rouen
- Université de Picardie Jules Verne
- CHU de Reims
- Université Grenoble Alpes
- CHU de Poitiers
- Université de Poitiers
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.
Details
Original language | English |
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Article number | 8290 |
Pages (from-to) | 1-10 |
Number of pages | 10 |
Journal | Nature communications |
Volume | 14 |
Issue number | 1 |
Publication status | Published - Dec 2023 |
Peer-reviewed | Yes |
External IDs
PubMed | 38092727 |
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