ArticleNature communications2023
Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 3 of them syntheses that pooled it.
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Who cites it
59 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Assessment of the Diagnostic Performance and Clinical Impact of AI in Hepatic Steatosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- Comprehensive overview of artificial intelligence in surgery: a systematic review and perspectives.Pflugers Archiv : European journal of physiology · 2025Pooled it
- Accurate pathologic classification of aggressive myocarditis improves insights into patient precise medicine.Fundamental research · 2026Article
- Comprehensive molecular profiling of combined hepatocellular carcinoma and cholangiocarcinoma reveals distinct Notch signaling subgroups with prognostic significance.Virchows Archiv : an international journal of pathology · 2026Article
- Spatial transcriptomics in cancer research: insights into tumorigenesis, diagnosis and therapeutics.Cell death discovery · 2026Review
- [Artificial intelligence-assisted diagnostics in the pathology of internal and oncological diseases].Innere Medizin (Heidelberg, Germany) · 2026Review
- Deep learning based histomorphological phenotyping and prognostic stratification for combined SCLC and LCNEC.NPJ digital medicine · 2026Article
- Advances in In Vitro Diagnostics for Cholangiocarcinoma: From Biomarker Discovery to Artificial Intelligence.International journal of molecular sciences · 2026Review
- Heterogeneous Intermediate Phenotypes of Cancer Cells with Varying Ki-67-Positivity Rates, Including Histologically HCC-like and NEC-like Cells, in Liver MiNEN.International journal of molecular sciences · 2026Article
- Intratumoral heterogeneity of CT enhancement for component prediction and prognostic significance in combined hepatocellular carcinoma‑cholangiocarcinoma.European radiology · 2026Article
- Single-cell transcriptomic mapping of patient-derived primary liver cancer organoids reveals molecular subtypes and guides precision drug targeting.Cellular oncology (Dordrecht, Netherlands) · 2026Article
- Systemic therapy for combined hepatocellular-cholangiocarcinoma: a comprehensive review of chemotherapy, immunotherapy, and targeted therapy.Journal of liver cancer · 2026Review
- Prognostic model for pancreatic cancer based on machine learning of routine slides and transcriptomic tumor analysis.British journal of cancer · 2026Article
- Combined hepatocellular-cholangiocarcinoma: a contemporary pathologic and molecular perspective.Journal of liver cancer · 2026Review
- Systemic treatment strategies for combined hepatocellular-cholangiocarcinoma: current evidence and future direction.Journal of liver cancer · 2026Review
- Integrative Genomic and AI Approaches to Lung Cancer and Implications for Disease Prevention in Former Smokers.International journal of molecular sciences · 2026Review
- Beyond the convergence of metabolic reprogramming in primary liver cancer: a comprehensive review on energy and lipids metabolism.Frontiers in cell and developmental biology · 2026Review
- Spatial omics for profiling the dynamic tumor microenvironment.Clinical & translational immunology · 2026Review
- Preoperative 3D morphological phenotyping of hepatocellular carcinoma via a foundation model: a non-invasive biomarker for recurrence risk stratification.eGastroenterology · 2026Article
Corrections and comments
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85 authors.
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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.
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