ReviewWorld journal of hepatology2021
Deep learning in hepatocellular carcinoma: Current status and future perspectives.
Review in World journal of hepatology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancing Diagnostic Accuracy in Liver Cancer: A Systematic Review of Artificial Intelligence Applications in Hepatocellular Carcinoma and Cholangiocarcinoma Detection Using Abdominal CT Imaging.Asian Pacific journal of cancer prevention : APJCP · 2026Pooled it
- Comparative analysis of radiomics and deep-learning algorithms for survival prediction in hepatocellular carcinoma.Scientific reports · 2024Trial
- From black-box prediction to transparent insight: the status quo and paradigm shift of explainable artificial intelligence in hepatocellular carcinoma research.Journal of the Egyptian National Cancer Institute · 2026Review
- Improving surgical treatments for hepatocellular carcinoma.Nature reviews. Gastroenterology & hepatology · 2026Review
- An intelligent healthcare framework for hepatocellular carcinoma diagnosis based on aggregated learners from biomedical data utilising explainable artificial intelligence.Scientific reports · 2026Article
- Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation.World journal of gastrointestinal oncology · 2026Article
- Current Immunotherapy Strategies and Emerging Biomarkers for the Treatment of Hepatocellular Carcinoma.Cancers · 2025Review
- Comprehensive treatment of intermediate and advanced primary hepatocellular carcinoma based on transcatheter arterial chemoembolization (Review).Oncology letters · 2025Review
- The efficacy and safety of thymosin alpha-1 combined with lenvatinib plus sintilimab in unresectable hepatocellular carcinoma: a retrospective study.Scientific reports · 2025Article
- Persisting challenges in the early detection of hepatocellular carcinoma.Expert review of anticancer therapy · 2025Review
- Interventional Oncology Meets Immuno-oncology: Combination Therapies for Hepatocellular Carcinoma.Radiology · 2024Review
- Advanced Prediction of Hepatic Oncogenic Transformation in HBV Patients via RNA-Seq Data Analysis and Deep Learning Techniques.International journal of molecular sciences · 2024Article
- Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study.Surgical endoscopy · 2024Article
- From prediction to prevention: Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring.World journal of gastroenterology · 2024Article
- Prognostic Factors for Mortality in Hepatocellular Carcinoma at Diagnosis: Development of a Predictive Model Using Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.Frontiers in artificial intelligence · 2024Review
- Development and Validation of Deep Learning Model for Intermediate-Stage Hepatocellular Carcinoma Survival with Transarterial Chemoembolization (MC-hccAI 002): a Retrospective, Multicenter, Cohort Study.Journal of Cancer · 2024Article
- Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records.medRxiv : the preprint server for health sciences · 2023Article
- Suppression of hepatocellular carcinoma by Ulva lactuca ulvan via gut microbiota and metabolite interactions.Journal of advanced research · 2023Article
- Key therapeutic targets implicated at the early stage of hepatocellular carcinoma identified through machine-learning approaches.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma (HCC) is among the leading causes of cancer incidence and death. Despite decades of research and development of new treatment options, the overall outcomes of patients with HCC continue to remain poor. There are areas of unmet need in risk prediction, early diagnosis, accurate prognostication, and individualized treatments for patients with HCC. Recent years have seen an explosive growth in the application of artificial intelligence (AI) technology in medical research, with the field of HCC being no exception. Among the various AI-based machine learning algorithms, deep learning algorithms are considered state-of-the-art techniques for handling and processing complex multimodal data ranging from routine clinical variables to high-resolution medical images. This article will provide a comprehensive review of the recently published studies that have applied deep learning for risk prediction, diagnosis, prognostication, and treatment planning for patients with HCC.
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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.