ArticleCancer management and research2021
Radiomics Model Based on Gadoxetic Acid Disodium-Enhanced MR Imaging to Predict Hepatocellular Carcinoma Recurrence After Curative Ablation.
Article in Cancer management and research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 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
- Radiomics for preoperative prediction of early recurrence in hepatocellular carcinoma: a meta-analysis.Frontiers in oncology · 2023Pooled it
- Beyond Diagnosis: Magnetic Resonance Imaging-Based Imaging Biomarkers for Tumor Biology, Prognosis, and Clinical Decision-Making in Hepatocellular Carcinoma.Korean journal of radiology · 2026Review
- MRI radiomics andBMC cancer · 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
- The Potential of Artificial Intelligence to Improve Selection Criteria for Liver Transplantation in HCC.Cancers · 2025Review
- Review
- MRI Imaging Biomarkers for Prognostication of Hepatocellular Carcinoma.Journal of the Korean Society of Radiology · 2025Review
- Article
- Systematic Review: AI Applications in Liver Imaging with a Focus on Segmentation and Detection.Life (Basel, Switzerland) · 2025Review
- Deep-learning radiomics and hand-crafted radiomics utilizing contrast-enhanced MRI to predict early peritumoral recurrence after DEB-TACE with hepatocellular carcinoma: a two-center study.Frontiers in oncology · 2025Article
- Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score.World journal of gastroenterology · 2024Review
- Advancing Hepatocellular Carcinoma Management Through Peritumoral Radiomics: Enhancing Diagnosis, Treatment, and Prognosis.Journal of hepatocellular carcinoma · 2024Review
- Preoperative prediction for early recurrence of hepatocellular carcinoma using machine learning-based radiomics.Frontiers in oncology · 2024Article
- Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics.Infectious agents and cancer · 2023Review
- Role of gadoxetic acid-enhanced liver magnetic resonance imaging in the evaluation of hepatocellular carcinoma after locoregional treatment.World journal of gastroenterology · 2022Review
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA practical prognostic prediction model is absent for hepatocellular carcinoma (HCC) patients after curative ablation. We aimed to develop a radiomics model based on gadoxetic acid disodium-enhanced magnetic resonance (MR) images to predict HCC recurrence after curative ablation.
methodsWe retrospectively enrolled 132 patients with HCC who underwent curative ablation. Patients were randomly divided into the training (n = 92) and validation (n = 40) cohorts. Radiomic features were extracted from gadoxetic acid disodium-enhanced MR images of the liver before curative ablation, and various baseline clinical characteristics were collected. Cox regression and random survival forests were used to construct models that incorporated radiomic features and/or clinical characteristics. The predictive performance of the different models was compared using the concordance index (C-index) and decision curves analysis (DCA). A cutoff derived from the combined model was used for risk categorization, and recurrence-free survival (RFS) was compared between groups using the Kaplan-Meier survival curve analysis.
resultsTwenty radiomic features and four clinical characteristics were identified and used for model construction. The radiomics model constructed by tumoral and peritumoral radiomic features had better predictive performance (C-index 0.698, 95% confidence interval [CI] 0.640-0.755) compared with the clinical model (C-index 0.614, 95% CI 0.499-0.695), while the combined model had the best predictive performance (C-index 0.706, 95% CI 0.638-0.763). A better net benefit was observed with the combined model compared with the other two models according to the DCA. Distinct RFS distributions were observed when patients were categorized based on the cutoff derived from the combined model (Log rank test, p = 0.007).
conclusionThe radiomics model which combined radiomic features extracted from gadoxetic acid disodium-enhanced MR images with clinical characteristics could predict HCC recurrence after curative ablation.
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