ArticleEuropean radiology2021
Fully automated prediction of liver fibrosis using deep learning analysis of gadoxetic acid-enhanced MRI.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 76 citations in OpenAlex.
- Diagnosis of Liver Fibrosis Using Artificial Intelligence: A Systematic Review.Medicina (Kaunas, Lithuania) · 2023Pooled it
- Advanced liver fibrosis detection using a two-stage deep learning approach on standard T2-weighted MRI.Abdominal radiology (New York) · 2026Article
- Research Progress on the Application of Radiomics and Deep Learning in Liver Fibrosis.Journal of imaging · 2026Review
- Emerging Role of MRI-Based Artificial Intelligence in Individualized Treatment Strategies for Hepatocellular Carcinoma: A Narrative Review.Journal of magnetic resonance imaging : JMRI · 2026Review
- Artificial intelligence in hepatopathy diagnosis and treatment: Big data analytics, deep learning, and clinical prediction models.World journal of gastroenterology · 2025Review
- Meta-Analysis of AI Integration in Abdominal Imaging for Liver Fibrosis and MASLD: Evaluating Diagnostic Accuracy and Clinical Impact.Journal of clinical medicine · 2025Review
- Automated liver magnetic resonance elastography quality control and liver stiffness measurement using deep learning.Abdominal radiology (New York) · 2025Article
- Review
- Deep learning-based automated assessment of hepatic fibrosis via magnetic resonance images and nonimage data.Quantitative imaging in medicine and surgery · 2025Article
- Multi-site, multi-vendor development and validation of a deep learning model for liver stiffness prediction using abdominal biparametric MRI.European radiology · 2025Article
- CT-based artificial intelligence system complementing deep learning model and radiologist for liver fibrosis staging.iScience · 2025Article
- Utilizing Machine Learning to Predict Liver Allograft Fibrosis by Leveraging Clinical and Imaging Data.Clinical transplantation · 2025Article
- Fully automated MRI-based convolutional neural network for noninvasive diagnosis of cirrhosis.Insights into imaging · 2024Article
- Development of fully automated models for staging liver fibrosis using non-contrast MRI and artificial intelligence: a retrospective multicenter study.EClinicalMedicine · 2024Article
- Diagnostic performance of EfficientNetV2-S method for staging liver fibrosis based on multiparametric MRI.Heliyon · 2024Article
- Noninvasive diagnosis of liver cirrhosis: qualitative and quantitative imaging biomarkers.Abdominal radiology (New York) · 2024Review
- Accurate Liver Fibrosis Detection Through Hybrid MRMR-BiLSTM-CNN Architecture with Histogram Equalization and Optimization.Journal of imaging informatics in medicine · 2024Article
- Automated classification of liver fibrosis stages using ultrasound imaging.BMC medical imaging · 2024Article
- Advances in Noninvasive Molecular Imaging Probes for Liver Fibrosis Diagnosis.Biomaterials research · 2024Review
- Simulation and Optimization: A New Direction in Supercritical Technology Based Nanomedicine.Bioengineering (Basel, Switzerland) · 2023Review
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Authors and funding
8 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo (1) develop a fully automated deep learning (DL) algorithm based on gadoxetic acid-enhanced hepatobiliary phase (HBP) MRI and (2) compare the diagnostic performance of DL vs. MR elastography (MRE) for noninvasive staging of liver fibrosis.
methodsThis single-center retrospective study included 355 patients (M/F 238/117, mean age 60 years; training, n = 178; validation, n = 123; test, n = 54) who underwent gadoxetic acid-enhanced abdominal MRI, including HBP and MRE, and pathological evaluation of the liver within 1 year of MRI. Cropped liver HBP images from a custom-written fully automated liver segmentation were used as input for DL. A transfer learning approach based on the ImageNet VGG16 model was used. Different DL models were built for the prediction of fibrosis stages F1-4, F2-4, F3-4, and F4. ROC analysis was performed to evaluate the performance of DL in training, validation, and test sets and of MRE liver stiffness in the test set.
resultsAUC values of DL were 0.99/0.70/0.77 (F1-4), 0.92/0.71/0.91 (F2-4), 0.91/0.78/0.90 (F3-4), and 0.98/0.83/0.85 (F4) for training/validation/test sets, respectively. The AUCs of MRE liver stiffness in the test set were 0.86 (F1-4), 0.87 (F2-4), 0.92 (F3-4), and 0.86 (F4). AUCs of MRE and DL were not significantly different for any of the fibrosis stages (p > 0.134).
conclusionsThe fully automated DL models based on HBP gadoxetic acid MRI showed good-to-excellent diagnostic performance for staging of liver fibrosis, with similar diagnostic performance to MRE. After validation in independent sets, the DL algorithm may allow for noninvasive liver fibrosis assessment without the need for additional MRI hardware. KEY POINTS: • The developed deep learning algorithm, based on routine standard-of-care gadoxetic acid-enhanced MRI data, showed good-to-excellent diagnostic performance for noninvasive staging of liver fibrosis. • The diagnostic performance of the deep learning algorithm was equivalent to that of MR elastography in a separate test set.
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