SynthesisBMC medical imaging2024
Artificial intelligence in predicting recurrence after first-line treatment of liver cancer: a systematic review and meta-analysis.
Synthesis in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- AI-Driven Predictive Models of Early Recurrence of HCC After Surgical Resection: A Systematic Review.Cancers · 2026Review
- A Machine Learning Framework for Prognostic Modeling in Stage III Colon Cancer.Journal of clinical medicine · 2026Article
- Effectiveness and Safety of Atezolizumab Plus Bevacizumab in Unresectable Hepatocellular Carcinoma: A Multicenter, Retrospective Real-World Study in China.Clinical Medicine Insights. Oncology · 2026Article
- Evaluating the clinical utility of large language models for hepatocellular carcinoma treatment recommendations: A nationwide retrospective registry study.PLoS medicine · 2026Article
- Performance of artificial intelligence in predicting hepatocellular carcinoma recurrence after thermal ablation: A systematic review.World journal of hepatology · 2025Article
- Treatment of recurrent hepatocellular carcinoma: The current standards and future perspectives.World journal of gastrointestinal oncology · 2025Review
- Review
- Colorectal cancer liver metastases: A radiologic point of view.World journal of gastrointestinal oncology · 2025Article
- Recent Advances in Magnetic Resonance Imaging for the Diagnosis of Liver Cancer: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Towards understanding cancer dormancy over strategic hitching up mechanisms to technologies.Molecular cancer · 2025Review
- Improving early liver metastasis detection in colorectal cancer using a weighted ensemble of ResNet50 and swin transformer: a KHCC study.Frontiers in big data · 2025Article
- The use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinoma.Frontiers in medicine · 2025Review
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Authors and funding
8 authors.
Funding
Abstract
backgroundThe aim of this study was to conduct a systematic review and meta-analysis to comprehensively evaluate the performance and methodological quality of artificial intelligence (AI) in predicting recurrence after single first-line treatment for liver cancer.
methodsA rigorous and systematic evaluation was conducted on the AI studies related to recurrence after single first-line treatment for liver cancer, retrieved from the PubMed, Embase, Web of Science, Cochrane Library, and CNKI databases. The area under the curve (AUC), sensitivity (SENC), and specificity (SPEC) of each study were extracted for meta-analysis.
resultsSix percutaneous ablation (PA) studies, 16 surgical resection (SR) studies, and 5 transarterial chemoembolization (TACE) studies were included in the meta-analysis for predicting recurrence after hepatocellular carcinoma (HCC) treatment, respectively. Four SR studies and 2 PA studies were included in the meta-analysis for recurrence after intrahepatic cholangiocarcinoma (ICC) and colorectal cancer liver metastasis (CRLM) treatment. The pooled SENC, SEPC, and AUC of AI in predicting recurrence after primary HCC treatment via PA, SR, and TACE were 0.78, 0.90, and 0.92; 0.81, 0.77, and 0.86; and 0.73, 0.79, and 0.79, respectively. The values for ICC treated with SR and CRLM treated with PA were 0.85, 0.71, 0.86 and 0.69, 0.63,0.74, respectively.
conclusionThis systematic review and meta-analysis demonstrates the comprehensive application value of AI in predicting recurrence after a single first-line treatment of liver cancer, with satisfactory results, indicating the clinical translation potential of AI in predicting recurrence after liver cancer treatment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.