Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma.
Nat Commun
; 14(1): 8290, 2023 Dec 14.
Article
in En
| MEDLINE
| ID: mdl-38092727
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Bile Duct Neoplasms
/
Cholangiocarcinoma
/
Carcinoma, Hepatocellular
/
Deep Learning
/
Liver Neoplasms
Limits:
Humans
Language:
En
Journal:
Nat Commun
Journal subject:
BIOLOGIA
/
CIENCIA
Year:
2023
Document type:
Article
Affiliation country:
France