ArticleHeliyon2025
Magnetic resonance imaging radiomics based on artificial intelligence is helpful to evaluate the prognosis of single hepatocellular carcinoma.
Article in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Independent Prognosis Prediction of Hepatocellular Carcinoma Using MicroRNA-Based Single and Combined Biomarkers.Canadian journal of gastroenterology & hepatology · 2026Review
- Mechanisms of ferroptosis in primary hepatocellular carcinoma and progress of artificial intelligence-based predictive modeling in hepatocellular carcinoma.World journal of gastroenterology · 2025Review
- Integrating radiomics and machine learning for the diagnosis and prognosis of hepatocellular carcinoma.World journal of gastrointestinal oncology · 2025Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Previous studies mostly use single-type features to establish a prediction model. We aim to develop a comprehensive prediction model that effectively identify patients with poor prognosis for single hepatocellular carcinoma (HCC) based on artificial intelligence (AI). Results: The clinical model based on linear regression (LR) algorithm (AUC: 0.658, 95%CI: 0.5021-0.8137), the radiomics model and deep transfer learning (DTL) model based on light gradient-boosting machine (Light GBM) algorithm (AUC: 0.761, 95%CI: 0.6326-0.8886 and AUC: 0.784, 95%CI: 0.6587-0.9087, respectively) were the optimal prediction models. A comparison revealed that the integrated nomogram had the largest area under the receiver operating characteristic curve (AUC) (all P < 0.05). In the training cohort, the integrated nomogram was predictive of recurrence-free survival (RFS) as well as overall survival (OS) (C-index: 0.735 and 0.712, P < 0.001). In the test cohort, the integrated nomogram also can predict RFS and OS (C-index: 0.718 and 0.740, P < 0.001) in patients. Conclusion: The integrated nomogram composed of signatures in the prediction models can not only predict the postoperative recurrence of single HCC patients but also stratify the risk of OS after the operation.
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Registered trials
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