ArticleFrontiers in oncology2022
Radiomics nomogram based on multi-parametric magnetic resonance imaging for predicting early recurrence in small hepatocellular carcinoma after radiofrequency ablation.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline 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
- Intra-ablational and peri-ablational radiomics based on post-RFA gadoxetic acid disodium-enhanced MRI for predicting short-term prognosis in hepatocellular carcinoma.Abdominal radiology (New York) · 2026Article
- MRI radiomics andBMC cancer · 2026Article
- Deciphering the predictors of endometrial nonbenign lesions in asymptomatic postmenopausal women via explainable machine learning.Menopause (New York, N.Y.) · 2026Article
- Predictors of True Early Recurrence in Patients Undergoing Radiofrequency Ablation for Hepatocellular Carcinoma.JGH open : an open access journal of gastroenterology and hepatology · 2026Article
- Predictive value of an AI model integrating MRI radiomics and clinical features for early recurrence of hepatocellular carcinoma prior to radiofrequency ablation.Frontiers in radiology · 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
- Risk Group Stratification for True Early Recurrence After Ablation for Hepatomas.Canadian journal of gastroenterology & hepatology · 2026Article
- Performance of artificial intelligence in predicting hepatocellular carcinoma recurrence after thermal ablation: A systematic review.World journal of hepatology · 2025Article
- Artificial Intelligence in Thermal Ablation: Current Applications and Future Directions in Microwave Technologies.Biomimetics (Basel, Switzerland) · 2025Review
- Review
- MRI Imaging Biomarkers for Prognostication of Hepatocellular Carcinoma.Journal of the Korean Society of Radiology · 2025Review
- DCE-MRI quantitative analysis and MRI-based radiomics for predicting the early efficacy of microwave ablation in lung cancers.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- The predictive value of contrast-enhanced ultrasound parameters combined with inflammatory nutritional indicators for recurrence and survival of early liver cancer following radiofrequency ablation.American journal of cancer research · 2025Article
- Predictive value of contrast-enhanced MRI for the regrowth of residual uterine fibroids after high-intensity focused ultrasound treatment.Insights into imaging · 2024Article
- Contributing to the prediction of prognosis for treated hepatocellular carcinoma: Imaging aspects that sculpt the future.World journal of gastrointestinal surgery · 2024Article
- Identification of texture MRI brain abnormalities on Fibromyalgia syndrome using interpretable machine learning models.Scientific reports · 2024Article
- [Research progress of radiomics in hepatocellular carcinoma].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2024Review
- Article
- Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score.World journal of gastroenterology · 2024Review
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Authors and funding
7 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Background: There are few studies on the application of radiomics in the risk prediction of early recurrence (ER) after radiofrequency ablation (RFA). This study evaluated the value of a multi-parametric magnetic resonance imaging (MRI, mpMRI)-based radiomics nomogram in predicting ER of small hepatocellular carcinoma (HCC) after RFA. Materials and methods: A retrospective analysis was performed on 90 patients with small HCC who were treated with RFA. Patients were divided into two groups according to recurrence within 2 years: the ER group (n=38) and the non-ER group (n=52). Preoperative T1WI, T2WI, and contrast-enhanced MRI (CE-MRI) were used for radiomic analysis. Tumor segmentation was performed on the images and applied to extract 1316 radiomics features. The most predictive features were selected using analysis of variance + Mann-Whitney, Spearman's rank correlation test, random forest (importance), and least absolute shrinkage and selection operator analysis. Radiomics models based on each sequence or combined sequences were established using logistic regression analysis. A predictive nomogram was constructed based on the radiomics score (rad-score) and clinical predictors. The predictive efficiency of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was used to evaluate the clinical efficacy of the nomogram. Results: The radiomics model mpMRI, which is based on T1WI, T2WI, and CE-MRI sequences, showed the best predictive performance, with an AUC of 0.812 for the validation cohort. Combined with the clinical risk factors of albumin level, number of tumors, and rad-score of mpMRI, the AUC of the preoperative predictive nomogram in the training and validation cohorts were 0.869 and 0.812, respectively. DCA demonstrated that the combined nomogram is clinically useful. Conclusions: The multi-parametric MRI-based radiomics nomogram has a high predictive value for ER of small HCC after RFA, which could be helpful for personalized risk stratification and further treatment decision-making for patients with small HCC.
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