ReviewJournal of hepatocellular carcinoma2022
Animal Models of Hepatocellular Carcinoma: Current Applications in Clinical Research.
Review in Journal of hepatocellular carcinoma, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 8 citations in OpenAlex.
- Etiology-Driven Mouse Models of Hepatocellular Carcinoma: Paving the Way for Precision Oncology.Journal of hepatocellular carcinoma · 2026Review
- Article
- Integrating molecular, biochemical, and immunohistochemical features as predictors of hepatocellular carcinoma drug response using machine-learning algorithms.Frontiers in molecular biosciences · 2024Article
- Obesity, non-alcoholic fatty liver disease and hepatocellular carcinoma: current status and therapeutic targets.Frontiers in endocrinology · 2023Review
- Exploring the Impact of the β-Catenin Mutations in Hepatocellular Carcinoma: An In-Depth Review.Cancer control : journal of the Moffitt Cancer CenterReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the last decade, relevant advances have occurred in the treatment of hepatocellular carcinoma (HCC), with novel drugs entering the clinical practice, among which tyrosine kinase inhibitors (TKIs) such as lenvatinib, cabozantinib and regorafenib, and immune checkpoint inhibitors (ICPIs) either alone or in combination with VEGF inhibitors. Clinical trials have driven the introduction of such novel molecules into the clinics but, at present, no biomarker drives the choice of first-line options, which relies only upon clinical and imaging assessment. Remarkably, clinical and imaging-based evaluations do not consider the huge heterogeneity of HCC and do not allow to realize the potential of personalized treatments. Preclinical research still does not inform the design of clinical trials, even though many animal models mimicking specific subgroups of HCC are available and might provide relevant information. Although animal models directly informing the clinical practice, such as patients-derived xenografts, are not used to help the choice of treatment in advanced HCC, however, the preclinical research can count on a wide range of valuable models. Here we will review some HCC models which might turn informative for specific questions in defined patient subgroups, and we will describe recent preclinical studies for the mechanistic evaluation of immunotherapy-based treatment approaches. To this aim, we will mainly focus on two issues: (i) HCC models informative on NAFLD-NASH HCC and (ii) HCC models helping to elucidate mechanisms underneath immunotherapy. We have chosen these two settings since they represent, respectively, the most rapidly arising cause of chronic liver disease (CLD) and HCC in western countries and the most promising therapeutic option for advanced HCC.
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.