ReviewKorean journal of radiology2020
Radiomics and Deep Learning: Hepatic Applications.
Review in Korean journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers, 6 of them syntheses 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
94 citing papers in PubMed, 6 syntheses or guidelines pooled it, 155 citations in OpenAlex.
- Machine Learning in Predicting the Risk of Esophagogastric Variceal Bleeding Among Patients With Liver Cirrhosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Computed tomography-based artificial intelligence for predicting preoperative microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis.La Radiologia medica · 2026Pooled it
- Multi-omics and high-spatial-resolution omics: deciphering complexity in neurological disorders.GigaScience · 2025Pooled it
- Role of radiomics in staging liver fibrosis: a meta-analysis.BMC medical imaging · 2024Pooled it
- Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review.European radiology · 2023Pooled it
- Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy.European journal of nuclear medicine and molecular imaging · 2022Pooled it
- Development and validation of a radiomics model of magnetic resonance for predicting liver metastasis in resectable pancreatic ductal adenocarcinoma patients.Radiation oncology (London, England) · 2023Trial
- Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors.Scientific reports · 2021Trial
- Enhancing post-TIPS hepatic encephalopathy risk stratification: a hybrid TabPFN model leveraging radiomics, deep transfer learning features, and MELD score.Hepatology international · 2026Article
- Radiomics differences between GOLD I-II and GOLD III-IV in patients with chronic obstructive pulmonary disease.Journal of thoracic disease · 2026Article
- Combining ultrasound and core needle histology imaging Radiopathomics model in predicting axillary lymph node metastasis in triple-negative breast cancer.World journal of surgical oncology · 2026Article
- Machine-learning with multiomics for radiotherapy-induced acute rectal toxicities and survival outcome in patients with cervical cancer.Radiation oncology (London, England) · 2026Article
- An Integrated QSM-Radiomics Nomogram With Clinical and Imaging Markers for Stratifying Cognitive Impairment in Hypertension.CNS neuroscience & therapeutics · 2026Article
- Establishment and validation of a CT-based clinical deep learning radiomics nomogram for predicting the response to transcatheter arterial chemoembolization in patients with hepatocellular carcinoma.Frontiers in oncology · 2026Article
- Article
- Hepatic enhancement and signal intensity analysis on magnetic resonance imaging as prognostic biomarkers in advanced chronic liver disease.World journal of hepatology · 2025Article
- Radiomic Analysis Applied to Pretreatment Gd-EOB-DTPA-enhanced MR Imaging Predicts Response to Selective Transcatheter Arterial Chemoembolization for Hepatocellular Carcinoma.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Artificial Intelligence in Thermal Ablation: Current Applications and Future Directions in Microwave Technologies.Biomimetics (Basel, Switzerland) · 2025Review
- Deep Learning-Assisted Diagnosis of Placenta Accreta Spectrum Using the DenseNet-121 Model: A Multicenter, Retrospective Study.Journal of imaging informatics in medicine · 2025Article
- Engineering macrophage responses through 3D scaffold microarchitecture.Materials today. Bio · 2025Article
34 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
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant hepatic tumors, and segmenting the liver and liver tumors. In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease.
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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.