ReviewInternational journal of molecular sciences2023
Liver Organoids as an In Vitro Model to Study Primary Liver Cancer.
Review in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Development, Expansion, and Histological Characterization of Patient-Derived Liver Organoids for Drug Screening and Disease Modeling.Bio-protocol · 2026Article
- Constructing an ultrasound-assisted organoid model for tumor management.Discover oncology · 2025Review
- Establishment of a mouse hepatocellular carcinoma tumoroid panel recapitulating inter- and intra- heterogeneity for disease modelling and combinatorial drug discovery.Cell communication and signaling : CCS · 2025Article
- Biliary Injuries Repair Using Copolymeric Scaffold: A Systematic Review and In Vivo Experimental Study.Journal of functional biomaterials · 2025Article
- Reconstructing the hepatocellular carcinoma microenvironment: the current status and challenges of 3D culture technology.Discover oncology · 2025Review
- Engineering liver disease models in vitro: emerging trends and innovations.eGastroenterology · 2025Review
- Applications of 3D models in cholangiocarcinoma.Frontiers in oncology · 2025Review
- Tumor organoids for primary liver cancers: A systematic review of current applications in diagnostics, disease modeling, and drug screening.JHEP reports : innovation in hepatology · 2024Article
- Emerging role of molecular diagnosis and personalized therapy for hepatocellular carcinoma.iLIVER · 2024Review
- Review
- Hepatocellular Carcinoma: Latest Research in Pathogenesis, Detection and Treatment.International journal of molecular sciences · 2023Article
- From Non-Alcoholic Fatty Liver Disease to Liver Cancer: Microbiota and Inflammation as Key Players.Pathogens (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Primary liver cancers (PLC), including hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), are among the leading causes of cancer-related mortality worldwide. Bi-dimensional in vitro models are unable to recapitulate the key features of PLC; consequently, recent advancements in three-dimensional in vitro systems, such as organoids, opened up new avenues for the development of innovative models for studying tumour's pathological mechanisms. Liver organoids show self-assembly and self-renewal capabilities, retaining essential aspects of their respective in vivo tissue and allowing modelling diseases and personalized treatment development. In this review, we will discuss the current advances in the field of liver organoids focusing on existing development protocols and possible applications in regenerative medicine and drug discovery.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.