ArticleWorld journal of gastroenterology2024
Leveraging machine learning for early recurrence prediction in hepatocellular carcinoma: A step towards precision medicine.
Article in World journal of gastroenterology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- High-sensitivity photoelectric sensing of apatinib for recurrent hepatocellular carcinoma in TACE.RSC advances · 2026Article
- Interpretable AI-driven prediction of early postoperative recurrence in locally advanced rectal cancer using the Systemic Inflammatory-Nutritional Index (SINTI): A multi-center study and clinical web tool.International journal of colorectal disease · 2026Article
- Radiomics analysis based on dynamic contrast-enhanced MRI for predicting early recurrence after hepatectomy in hepatocellular carcinoma patients.Scientific reports · 2025Article
- New frontiers in hepatocellular carcinoma: Precision imaging for microvascular invasion prediction.World journal of gastroenterology · 2025Article
- The role of vitamin D in sleep regulation: mechanisms, clinical advances, and future directions.Frontiers in nutrition · 2025Review
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The high rate of early recurrence in hepatocellular carcinoma (HCC) post curative surgical intervention poses a substantial clinical hurdle, impacting patient outcomes and complicating postoperative management. The advent of machine learning provides a unique opportunity to harness vast datasets, identifying subtle patterns and factors that elude conventional prognostic methods. Machine learning models, equipped with the ability to analyse intricate relationships within datasets, have shown promise in predicting outcomes in various medical disciplines. In the context of HCC, the application of machine learning to predict early recurrence holds potential for personalized postoperative care strategies. This editorial comments on the study carried out exploring the merits and efficacy of random survival forests (RSF) in identifying significant risk factors for recurrence, stratifying patients at low and high risk of HCC recurrence and comparing this to traditional COX proportional hazard models (CPH). In doing so, the study demonstrated that the RSF models are superior to traditional CPH models in predicting recurrence of HCC and represent a giant leap towards precision medicine.
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Registered trials
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