ReviewAbdominal radiology (New York)2021
Radiomics of hepatocellular carcinoma.
Review in Abdominal radiology (New York), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 4 of them syntheses that pooled it.
What it found
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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
49 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Magnetic resonance imaging radiomics for predicting hepatocellular carcinoma recurrence following resection or ablation: a systematic review and meta-analysis.Abdominal radiology (New York) · 2026Pooled it
- CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis.Frontiers in oncology · 2022Pooled it
- Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma.Alimentary pharmacology & therapeutics · 2021Pooled it
- Using pre-operative radiomics to predict microvascular invasion of hepatocellular carcinoma based on Gd-EOB-DTPA enhanced MRI.BMC medical imaging · 2022Trial
- Comprehensive radiomics nomogram for predicting survival of patients with combined hepatocellular carcinoma and cholangiocarcinoma.World journal of gastroenterology · 2021Trial
- Artificial intelligence in contrast enhanced ultrasound: A new era for liver lesion assessment.World journal of gastroenterology · 2025Review
- A T2 weighted imaging-based radiomics nomogram for the classification of hepatic blood-rich lesions: hepatocellular carcinoma and benign liver lesions.Discover oncology · 2025Article
- A magnetic resonance imaging radiomics approach predicts hepatocellular carcinoma response to stereotactic body radiation therapy.Physics and imaging in radiation oncology · 2025Article
- Article
- Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach.Abdominal radiology (New York) · 2025Article
- Predictive factors and prognostic models for Hepatic arterial infusion chemotherapy in Hepatocellular carcinoma: a comprehensive review.World journal of surgical oncology · 2025Review
- Review
- [Research progress of radiomics in hepatocellular carcinoma].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2024Review
- Predictability of the radiological response to Yttrium-90 transarterial radioembolization by dynamic magnetic resonance imaging-based radiomics analysis in patients with intrahepatic cholangiocarcinomaDiagnostic and interventional radiology (Ankara, Turkey) · 2024Article
- Development of a multi-phase CT-based radiomics model to differentiate heterotopic pancreas from gastrointestinal stromal tumor.BMC medical imaging · 2024Article
- Predicting treatment responses using magnetic resonance imaging-based radiomics in hepatocellular carcinoma patients undergoing transarterial radioembolization.Revista da Associacao Medica Brasileira (1992) · 2024Article
- Advancing Hepatocellular Carcinoma Management Through Peritumoral Radiomics: Enhancing Diagnosis, Treatment, and Prognosis.Journal of hepatocellular carcinoma · 2024Review
- Machine Learning Radiomics Signature for Differentiating Lymphoma versus Benign Splenomegaly on CT.Diagnostics (Basel, Switzerland) · 2023Article
- Consensus report from the 10th Global Forum for Liver Magnetic Resonance Imaging: developments in HCC management.European radiology · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The diagnosis of hepatocellular carcinoma relies largely on non-invasive imaging, and is well suited for radiomics analysis. Radiomics is an emerging method for quantification of tumor heterogeneity by mathematically analyzing the spatial distribution and relationships of gray levels in medical images. The published studies on radiomics analysis of HCC provide encouraging data demonstrating potential utility for prediction of tumor biology, molecular profiles, post-therapy response, and outcome. The combination of radiomics data and clinical/laboratory information provides added value in many studies. Radiomics is a multi-step process that requires optimization and standardization, the development of semi-automated or automated segmentation methods, robust data quality control, and refinement of algorithms and modeling approaches for high-throughput data analysis. While radiomics remains largely in the research setting, the strong associations of predictive models and nomograms with certain pathologic, molecular, and immune markers with tumor aggressiveness and patient outcomes, provide great potential for clinical applications to inform optimized treatment strategies and patient prognosis.
Indexed as
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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.