Article

Self-supervised learning to predict intrahepatic cholangiocarcinoma transcriptomic classes on routine histology

2024-01-16

Abstract excerpt

<h4>ABSTRACT</h4> <h4>Objective</h4> The transcriptomic classification of intrahepatic cholangiocarcinomas (iCCA) has been recently refined from two to five classes, associated with pathological features, targetable genetic alterations and survival. Despite its prognostic and therapeutic value, the classification is not routinely used in the clinic because of technical limitations, including insufficient tissue...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 6105e8fc-36fe-5444-b5e1-edc32c68b3b3
DOI: 10.1101/2024.01.15.575652

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Self-supervised learning to predict intrahepatic cholangiocarcinoma transcriptomic classes on routine histologyDOI 10.1101/2024.01.15.575652Read

Single-Cell Transcriptomic Analysis Reveals Two Molecularly and Clinically Distinct Subtypes of Intrahepatic CholangiocarcinomaSemantic proximity Read

Machine learning for clinical decision support in intrahepatic cholangiocarcinoma based on a population study of the US SEER database and a Chinese single-center registrySemantic proximity Read

A transcriptome based molecular classification scheme for cholangiocarcinoma and subtype-derived prognostic biomarkerSemantic proximity Read

Heterogeneity of small duct- and large duct-type intrahepatic cholangiocarcinoma.Semantic proximity Read

Transcriptomic and histopathological analysis of cholangiolocellular differentiation trait in intrahepatic cholangiocarcinoma.Semantic proximity Read

Classification of early and late stage liver hepatocellular carcinoma patients from their genomics and epigenomics profilesSemantic proximity Read

Classification of early and late stage Liver Hepatocellular Carcinoma patients from their genomics and epigenomics profilesSemantic proximity Read

Beyond histology: self‑organizing map-derived MASLD transcriptomic subtypes for patient stratificationSemantic proximity Read

A noninvasive and highly efficient epigenetic predictive model for efficacy of the GOLP regimen in patients with intrahepatic cholangiocarcinomaSemantic proximity Read

Machine learning-based survival analysis and risk stratification for patients with cholangiocarcinomaSemantic proximity Read

Integrative analysis of multiple genomic data from intrahepatic cholangiocarcinoma organoids enables tumor subtypingSemantic proximity Read

Self-supervised artificial intelligence predicts recurrence, metastasis and disease specific death from primary cutaneous squamous cell carcinoma at diagnosisSemantic proximity Read