ReviewKorean journal of radiology2024
Prognostication of Hepatocellular Carcinoma Using Artificial Intelligence.
Review in Korean journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Beyond Diagnosis: Magnetic Resonance Imaging-Based Imaging Biomarkers for Tumor Biology, Prognosis, and Clinical Decision-Making in Hepatocellular Carcinoma.Korean journal of radiology · 2026Review
- Development and temporal validation of 1-, 2-, and 3-year survival prediction models for hepatocellular carcinoma using the SEER database.Scientific reports · 2026Article
- Artificial intelligence and radiomics on computed tomography for differentiating hepatocellular carcinoma and intrahepatic cholangiocarcinoma: a multimodal integration approach.BMC medical imaging · 2026Article
- Jianpi Jiedu Xiaozheng Fang Regulates Hepatocellular Carcinoma Proliferation and Metastasis Based on Network Pharmacology.Journal of cellular and molecular medicine · 2026Article
- An easily machine learning-based tool for preliminary risk assessment of microvascular invasion in hepatocellular carcinoma.Surgical endoscopy · 2025Article
- Preoperative prediction of early recurrence in hepatocellular carcinoma using simultaneous multislice diffusion kurtosis imaging.European radiology · 2025Article
- Performance of simultaneous multislice diffusion-weighted imaging using monoexponential, intravoxel incoherent motion, and diffusion kurtosis models: assessment of microvascular invasion and histologic grade in hepatocellular carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Radiologic and Pathologic Discordance in Hepatocellular Carcinoma: More Than a Mismatch, with Prognostic Significance.Gut and liver · 2025Article
- Evaluation of the efficacy of laparoscopic hepatolobectomy for primary liver cancer with early combined enteral and parenteral nutritional support.Discover oncology · 2025Article
- MRI Imaging Biomarkers for Prognostication of Hepatocellular Carcinoma.Journal of the Korean Society of Radiology · 2025Review
- Artificial intelligence-based evaluation of prognostic benefits from immunotherapy plus targeted therapy with or without radiotherapy or TACE in advanced hepatocellular carcinoma.Frontiers in oncology · 2025Article
- Development of a machine learning model to predict overall survival for large hepatocellular carcinoma at BCLC stage A or B after curative hepatectomy.Frontiers in immunology · 2025Article
- Construction of a 2.5D Deep Learning Model for Predicting Early Postoperative Recurrence of Hepatocellular Carcinoma Using Multi-View and Multi-Phase CT Images.Journal of hepatocellular carcinoma · 2024Article
- Radiomics-based differentiation of hepatocellular carcinoma and dysplastic nodules using noncontrast abbreviated MRI.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Hepatocellular carcinoma (HCC) is a biologically heterogeneous tumor characterized by varying degrees of aggressiveness. The current treatment strategy for HCC is predominantly determined by the overall tumor burden, and does not address the diverse prognoses of patients with HCC owing to its heterogeneity. Therefore, the prognostication of HCC using imaging data is crucial for optimizing patient management. Although some radiologic features have been demonstrated to be indicative of the biologic behavior of HCC, traditional radiologic methods for HCC prognostication are based on visually-assessed prognostic findings, and are limited by subjectivity and inter-observer variability. Consequently, artificial intelligence has emerged as a promising method for image-based prognostication of HCC. Unlike traditional radiologic image analysis, artificial intelligence based on radiomics or deep learning utilizes numerous image-derived quantitative features, potentially offering an objective, detailed, and comprehensive analysis of the tumor phenotypes. Artificial intelligence, particularly radiomics has displayed potential in a variety of applications, including the prediction of microvascular invasion, recurrence risk after locoregional treatment, and response to systemic therapy. This review highlights the potential value of artificial intelligence in the prognostication of HCC as well as its limitations and future prospects.
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Registered trials
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