ArticleGut2019
Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study.
Article in Gut, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 4 registered trials, which are not on this map. Cited by 255 papers, 10 of them syntheses that pooled it.
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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.
Evaluation of ShearWave™ Elastography Performances for the Non-Invasive Assessment of Liver Fibrosis in Chinese Patients With Chronic Hepatitis B Infection
MR-based Models for Noninvasive Detection of Clinically Significant Portal Hypertension in Cirrhosis (CHESS1802)
Prediction of Decompensation and HCC Development in Patients With Advanced Chronic Liver Disease by the PLEASE and M10S20 Algorithms
Study on the Staging and Prognosis Model of Bladder Cancer Based on Artificial Intelligence and Multimodal Omics Features
Who cites it
255 citing papers in PubMed, 10 syntheses or guidelines pooled 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
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 2025Pooled it
- KASL clinical practice guidelines for noninvasive tests to assess liver fibrosis in chronic liver disease.Clinical and molecular hepatology · 2024Guideline
- Pooled it
- Role of radiomics in staging liver fibrosis: a meta-analysis.BMC medical imaging · 2024Pooled it
- Deep learning in voice analysis for diagnosing vocal cord pathologies: a systematic review.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Pooled it
- Diagnosis of Liver Fibrosis Using Artificial Intelligence: A Systematic Review.Medicina (Kaunas, Lithuania) · 2023Pooled it
- Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review.Reviews in endocrine & metabolic disorders · 2022Pooled it
- Diagnostic Accuracy of Artificial Intelligence Based on Imaging Data for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis.Frontiers in oncology · 2022Pooled it
- Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis.BMC gastroenterology · 2021Pooled it
- Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease.Renal failure · 2023Trial
- Predicting Post-hepatectomy Liver Failure Preoperatively for Child-Pugh A5 Hepatocellular Carcinoma Patients by Liver Stiffness.Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract · 2023Trial
- A nomogram based on shear wave elastography for assessment of renal fibrosis in patients with chronic kidney disease.Journal of nephrology · 2023Trial
- Radiomics nomograms based on R2* mapping and clinical biomarkers for staging of liver fibrosis in patients with chronic hepatitis B: a single-center retrospective study.European radiology · 2023Trial
- Radiation pneumonitis prediction after stereotactic body radiation therapy based on 3D dose distribution: dosiomics and/or deep learning-based radiomics features.Radiation oncology (London, England) · 2022Trial
- Hepatobiliary phase radiomics for identifying subclinical liver dysfunction in compensated chronic liver disease: development and external validation.Abdominal radiology (New York) · 2026Article
- Quantitative liver ultrasound: current status and challenges.Ultrasonography (Seoul, Korea) · 2026Article
- Interpretable Machine Learning Using Multimodal Ultrasound and MRI Features for Predicting Axillary Lymph Node Metastasis in Breast Cancer: A Dual-Center Retrospective Study.Journal of imaging informatics in medicine · 2026Article
- Ultrasound Elastography in Chronic Liver Disease: From Clinical Applications to Quality Assurance and Future Perspectives.Diagnostics (Basel, Switzerland) · 2026Review
- Multiparametric Abdominal Ultrasound as a Source of Imaging Biomarkers in Predictive and Personalized Medicine: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
195 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
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe aimed to evaluate the performance of the newly developed deep learning Radiomics of elastography (DLRE) for assessing liver fibrosis stages. DLRE adopts the radiomic strategy for quantitative analysis of the heterogeneity in two-dimensional shear wave elastography (2D-SWE) images.
designA prospective multicentre study was conducted to assess its accuracy in patients with chronic hepatitis B, in comparison with 2D-SWE, aspartate transaminase-to-platelet ratio index and fibrosis index based on four factors, by using liver biopsy as the reference standard. Its accuracy and robustness were also investigated by applying different number of acquisitions and different training cohorts, respectively. Data of 654 potentially eligible patients were prospectively enrolled from 12 hospitals, and finally 398 patients with 1990 images were included. Analysis of receiver operating characteristic (ROC) curves was performed to calculate the optimal area under the ROC curve (AUC) for cirrhosis (F4), advanced fibrosis (≥F3) and significance fibrosis (≥F2).
resultsAUCs of DLRE were 0.97 for F4 (95% CI 0.94 to 0.99), 0.98 for ≥F3 (95% CI 0.96 to 1.00) and 0.85 (95% CI 0.81 to 0.89) for ≥F2, which were significantly better than other methods except 2D-SWE in ≥F2. Its diagnostic accuracy improved as more images (especially ≥3 images) were acquired from each individual. No significant variation of the performance was found if different training cohorts were applied.
conclusionDLRE shows the best overall performance in predicting liver fibrosis stages compared with 2D-SWE and biomarkers. It is valuable and practical for the non-invasive accurate diagnosis of liver fibrosis stages in HBV-infected patients. TRIAL REGISTRATION NUMBER: NCT02313649; Post-results.
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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.