ReviewLiver international : official journal of the International Association for the Study of the Liver2020
Radiomics in liver diseases: Current progress and future opportunities.
Review in Liver international : official journal of the International Association for the Study of the Liver, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 1 of them a synthesis 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.
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Who cites it
78 citing papers in PubMed, 1 synthesis or guideline pooled it, 114 citations in OpenAlex.
- Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysis.BMC medical imaging · 2023Pooled it
- Radiomics based on fluoro-deoxyglucose positron emission tomography predicts liver fibrosis in biopsy-proven MAFLD: a pilot study.International journal of medical sciences · 2021Trial
- Radiomics in HCC: the right technology, asking the wrong questions.European radiology · 2026Article
- Simultaneous grading diagnosis of liver fibrosis, inflammation, and steatosis using multimodal quantitative ultrasound and artificial intelligence framework.Medical & biological engineering & computing · 2026Article
- Analysis of fetal MRI data reveals no effect on liver maturation following prenatal alcohol exposure.European radiology · 2026Article
- Radiomics-Based Assessment of Portal Hypertension Severity and Risk Stratification of Cirrhotic Patients Using Routine CT Scans.Liver international : official journal of the International Association for the Study of the Liver · 2026Article
- Development of a clinical-CT-radiomics nomogram for predicting endoscopic red color sign in cirrhotic patients with esophageal varices.Abdominal radiology (New York) · 2026Article
- Non-invasive ultrasound assessment of chronic liver disease: current position and future directions for a "one-stop" liver ultrasound approach.Insights into imaging · 2026Article
- Imaging Biomarkers in Radiotherapy.Cancers · 2026Review
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
- Redefining precision in biliary surgery: a paradigm shift from operative mastery to prognostic-driven strategy.Frontiers in surgery · 2026Article
- Early prediction of adverse outcomes in liver cirrhosis using a CT-based multimodal deep learning model.Abdominal radiology (New York) · 2026Article
- Application of multimodal fusion technology in early recurrence prediction and pathological analysis of hepatocellular carcinoma.World journal of gastrointestinal oncology · 2025Article
- Radiomics of Hepatocellular Carcinoma: Identifying Predictors of Microvascular Invasion Using Multi-Phase CT Analysis.Journal of personalized medicine · 2025Article
- Machine learning model-based approach using cellular proliferation marker expression for preoperative clinical decision-making in patients with hepatocellular carcinoma.World journal of gastrointestinal oncology · 2025Article
- Development and Validation of a Modality-Invariant 3D Swin U-Net Transformer for Liver and Spleen Segmentation on Multi-Site Clinical Bi-parametric MR Images.Journal of imaging informatics in medicine · 2025Article
- Function ofEJNMMI reports · 2025Article
- Radiomics Beyond Radiology: Literature Review on Prediction of Future Liver Remnant Volume and Function Before Hepatic Surgery.Journal of clinical medicine · 2025Review
- Radiomics-based automated machine learning for differentiating focal liver lesions on unenhanced computed tomography.Abdominal radiology (New York) · 2025Article
- New frontiers in hepatocellular carcinoma: Precision imaging for microvascular invasion prediction.World journal of gastroenterology · 2025Article
18 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Liver diseases, a wide spectrum of pathologies from inflammation to neoplasm, have become an increasingly significant health problem worldwide. Noninvasive imaging plays a critical role in the clinical workflow of liver diseases, but conventional imaging assessment may provide limited information. Accurate detection, characterization and monitoring remain challenging. With progress in quantitative imaging analysis techniques, radiomics emerged as an efficient tool that shows promise to aid in personalized diagnosis and treatment decision-making. Radiomics could reflect the heterogeneity of liver lesions via extracting high-throughput and high-dimensional features from multi-modality imaging. Machine learning algorithms are then used to construct clinical target-oriented imaging biomarkers to assist disease management. Here, we review the methodological process in liver disease radiomics studies in a stepwise fashion from data acquisition and curation, region of interest segmentation, liver-specific feature extraction, to task-oriented modelling. Furthermore, the applications of radiomics in liver diseases are outlined in aspects of diagnosis and staging, evaluation of liver tumour biological behaviours, and prognosis according to different disease type. Finally, we discuss the current limitations of radiomics in liver disease studies and explore its future opportunities.
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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.