ReviewJournal of hepatocellular carcinoma2021
Up-to-Date Role of CT/MRI LI-RADS in Hepatocellular Carcinoma.
Review in Journal of hepatocellular carcinoma, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 40 citations in OpenAlex.
- Review
- What is the risk of HCC in hypervascular LI-RADS 3 observations without ancillary features in routine clinical practice?Abdominal radiology (New York) · 2026Article
- Deep Learning-based Multiple Arterial Phase MRI: A Step toward Improved Hepatocellular Carcinoma Diagnosis.Radiology. Imaging cancer · 2026Article
- Leveraging Large Language Models for Accurate AO Fracture Classification from CT Text Reports.Journal of imaging informatics in medicine · 2026Article
- The disguised liver abscess closely resembled diffuse hepatocellular carcinoma with portal vein tumor thrombus: a case description and literature analysis.Quantitative imaging in medicine and surgery · 2026Article
- Alkaline Phosphatase-Targeted, Gadolinium-Labeled Nanoparticles for Enhanced Multimodal Imaging of Liver Cancer.ACS applied materials & interfaces · 2025Article
- Impact of deep learning reconstructions on image quality and liver lesion detectability in dual-energy CT: An anthropomorphic phantom study.Medical physics · 2025Article
- Predictive Radiomics-Based Model for Recurrence-Free Survival After Curative Resection in Patients with Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2025Article
- Leveraging large language models for accurate classification of liver lesions from MRI reports.Computational and structural biotechnology journal · 2025Article
- Non-contrast abbreviated MRI for the detection of hepatocellular carcinoma in patients with Liver Imaging Reporting and Data System LR-3 and LR-4 observations in MRI.The British journal of radiology · 2024Article
- Simplified liver imaging reporting and data system for the diagnosis of hepatocellular carcinoma on gadoxetic acid-enhanced magnetic resonance imaging.World journal of gastrointestinal oncology · 2024Article
- Multiparametric MRI-based intratumoral and peritumoral radiomics for predicting the pathological differentiation of hepatocellular carcinoma.Insights into imaging · 2024Article
- A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Impact of Pre-Liver Transplant Treatments on the Imaging Accuracy of HCC Staging and Their Influence on Outcomes.Cancers · 2024Article
- Evidence Supporting Diagnostic Value of Liver Imaging Reporting and Data System for CT- and MR Imaging-based Diagnosis of Hepatocellular Carcinoma: A Systematic Review and Meta-analysis.Journal of biomedical physics & engineering · 2024Review
- Threshold growth has a limited role in differentiating hepatocellular carcinoma from other focal hepatic lesions.BMC medical imaging · 2023Article
- Review
- Prognosis Prediction of Hepatocellular Carcinoma Based on Magnetic Resonance Imaging Features.Korean journal of radiology · 2023Review
- Synergistic Nanomedicine: Photodynamic, Photothermal and Photoimmune Therapy in Hepatocellular Carcinoma: Fulfilling the Myth of Prometheus?International journal of molecular sciences · 2023Review
- Machine Learning-Based CEMRI Radiomics Integrating LI-RADS Features Achieves Optimal Evaluation of Hepatocellular Carcinoma Differentiation.Journal of hepatocellular carcinoma · 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
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma (HCC) is a leading cause of mortality worldwide and a major healthcare burden in most societies. Computed tomography (CT) and magnetic resonance imaging (MRI) play a pivotal role in the medical care of patients with or at risk for hepatocellular carcinoma (HCC). When stringent imaging criteria are fulfilled, CT and MRI allow for diagnosis, staging, and assessment of response to treatment, without the need for invasive workup, and can inform clinical decision making. Owing to the central role of these imaging modalities in HCC management, standardization is essential to facilitate proper imaging technique, accurate interpretation, and clear communication among all stakeholders in both the clinical practice and research settings. The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system that provides standardization across the continuum of HCC imaging, including ordinal probabilistic approach for reporting that directs individualized management. This review discusses the up-to-date role of CT and MRI in HCC imaging from the LI-RADS perspective. It also provides a glimpse into the future by discussing how advances in knowledge and technology are likely to enrich the LI-RADS approach.
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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.