ArticleEuropean radiology2023
Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 3 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 3 syntheses or guidelines pooled it, 47 citations in OpenAlex.
- 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
- Accuracy of Medical Image-Based Deep Learning for Detecting Microvascular Invasion in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Artificial intelligence in predicting recurrence after first-line treatment of liver cancer: a systematic review and meta-analysis.BMC medical imaging · 2024Pooled it
- A CECT-Based 2PI System as a Novel Noninvasive Prognostic Tool for Hepatocellular Carcinoma: A Dual-Validation Study.Cancer science · 2026Article
- MRI radiomics combined with clinical factors to predict vessels encapsulating tumor clusters and early recurrence in hepatocellular carcinoma (≤ 3 cm).BMC medical imaging · 2026Article
- Reduced visceral adipose tissue area linked to early recurrence in HCC patients with HBV: a parallel mediation analysis.Abdominal radiology (New York) · 2026Article
- Habitat analysis and other AI technologies for Ki-67 prediction in hepatocellular carcinoma: a multi-center study.BMC cancer · 2026Article
- Multimodal data-driven prediction of postoperative recurrence and survival in hepatocellular carcinoma: a narrative review.Journal of gastrointestinal oncology · 2026Review
- A deep learning radiopathomic signature predicts recurrence risk of hepatocellular carcinoma after hepatectomy.Communications biology · 2026Article
- Machine learning integrating MRI and clinical features predicts early recurrence of hepatocellular carcinoma after resection.Scientific reports · 2026Article
- Emerging Role of MRI-Based Artificial Intelligence in Individualized Treatment Strategies for Hepatocellular Carcinoma: A Narrative Review.Journal of magnetic resonance imaging : JMRI · 2026Review
- Multiphasic CT-based multimodal deep learning model for predicting early hepatocellular carcinoma recurrence following liver transplantation.Frontiers in oncology · 2026Article
- Artificial Intelligence and Radiomics in Primary Liver Cancer Imaging: A Bibliometric and Visualized Analysis.Journal of hepatocellular carcinoma · 2026Article
- A Transformer-Based Deep Learning Model for predicting Early Recurrence in Hepatocellular Carcinoma After Hepatectomy Using Intravoxel Incoherent Motion Images.Journal of hepatocellular carcinoma · 2026Article
- Purification and pharmacokinetic study of gadoxetate isomers for enhanced rabbit liver MR imaging.PloS one · 2026Article
- Leveraging AI and Natural Compounds: Innovative Approaches in the Diagnosis and Treatment of Hepatocellular Carcinoma.Current pharmaceutical design · 2026Review
- Preoperative MRI features and clinical factors for predicting early recurrence of hepatocellular carcinoma after hepatectomy.Frontiers in oncology · 2026Article
- An integrated radiomics and deep learning model on multisequence MRI for preoperative prediction of lymphovascular space invasion in endometrial cancer.BMC medical imaging · 2025Article
- The value of using deep learning to predict proliferative hepatocellular carcinoma based on multiphasic magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2025Article
- Hepatocellular Carcinoma Risk Stratification for Cirrhosis Patients: Integrating Radiomics and Deep Learning Computed Tomography Signatures of the Liver and Spleen into a Clinical Model.Journal of clinical and translational hepatology · 2025Article
Corrections and comments
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Authors and funding
13 authors at 6 institutions in 2 countries.
Funding
Abstract
objectivesThe accurate prediction of post-hepatectomy early recurrence in patients with hepatocellular carcinoma (HCC) is crucial for decision-making regarding postoperative adjuvant treatment and monitoring. We aimed to explore the feasibility of deep learning (DL) features derived from gadoxetate disodium (Gd-EOB-DTPA) MRI, qualitative features, and clinical variables for predicting early recurrence.
methodsIn this bicentric study, 285 patients with HCC who underwent Gd-EOB-DTPA MRI before resection were divided into training (n = 195) and validation (n = 90) sets. DL features were extracted from contrast-enhanced MRI images using VGGNet-19. Three feature selection methods and five classification methods were combined for DL signature construction. Subsequently, an mp-MR DL signature fused with multiphase DL signatures of contrast-enhanced images was constructed. Univariate and multivariate logistic regression analyses were used to identify early recurrence risk factors including mp-MR DL signature, microvascular invasion (MVI), and tumor number. A DL nomogram was built by incorporating deep features and significant clinical variables to achieve early recurrence prediction.
resultsMVI (p = 0.039), tumor number (p = 0.001), and mp-MR DL signature (p < 0.001) were independent risk factors for early recurrence. The DL nomogram outperformed the clinical nomogram in the training set (AUC: 0.949 vs. 0.751; p < 0.001) and validation set (AUC: 0.909 vs. 0.715; p = 0.002). Excellent DL nomogram calibration was achieved in both training and validation sets. Decision curve analysis confirmed the clinical usefulness of DL nomogram.
conclusionThe proposed DL nomogram was superior to the clinical nomogram in predicting early recurrence for HCC patients after hepatectomy. KEY POINTS: • Deep learning signature based on Gd-EOB-DTPA MRI was the predominant independent predictor of early recurrence for hepatocellular carcinoma (HCC) after hepatectomy. • Deep learning nomogram based on clinical factors and Gd-EOB-DTPA MRI features is promising for predicting early recurrence of HCC. • Deep learning nomogram outperformed the conventional clinical nomogram in predicting early recurrence.
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