ReviewMedical oncology (Northwood, London, England)2020
A closer look to the new frontier of artificial intelligence in the percutaneous treatment of primary lesions of the liver.
Review in Medical oncology (Northwood, London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Artificial Intelligence in the Heart of Medicine: A Systematic Approach to Transforming Arrhythmia Care with Intelligent Systems.Current cardiology reviews · 2025Pooled it
- Tumor ablation: emerging uses, challenges, and strategic implementation. A green paper by the Network of Expertise in Cancer (JANE-2), High Tech Medical Resources, network on Physical Methods of Tumor Ablation.Radiology and oncology · 2026Review
- Article
- Artificial intelligence: Emerging player in the diagnosis and treatment of digestive disease.World journal of gastroenterology · 2022Review
- Image-Guided Percutaneous Ablation for Primary and Metastatic Tumors.Diagnostics (Basel, Switzerland) · 2022Review
- Image-guided percutaneous ablation for lung malignancies.Frontiers in oncology · 2022Review
- The Value of Nuclear Magnetic Resonance in Liver Nodular Lesions.Contrast media & molecular imaging · 2022Article
- Image-Guided Robotics for Standardized and Automated Biopsy and Ablation.Seminars in interventional radiology · 2021Review
- Radiomics in hepatic metastasis by colorectal cancer.Infectious agents and cancer · 2021Article
- Role of Machine Learning and Artificial Intelligence in Interventional Oncology.Current oncology reports · 2021Review
- Role of Fusion Imaging in Image-Guided Thermal Ablations.Diagnostics (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of thermal ablation is induction of tumor death by means of localized hyperthermia resulting in irreversible cellular damage. Ablative therapies are well-recognized treatment modalities for HCC lesions and are considered standard of care for HCC nodules < 3 cm in diameter in patients not suitable for surgery. Effective lesion treatment rely on complete target volume ablation. Technical limitations are represented by large (> 3 cm) or multicentric nodules as well as complex nodule location and poor lesion conspicuity. Artificial Intelligence (AI) is a general term referred to computational algorithms that can analyze data and perform complex tasks otherwise prerogative of Human Intelligence. AI has a variety of application in percutaneous ablation procedures such as Navigational software, Fusion Imaging, and robot-assisted ablation tools. Those instruments represent relative innovations in the field of Interventional Oncology and promising strategies to overcome actual limitations of ablative therapy in order to increase feasibility and technical results. This work aims to review the principal application of Artificial Intelligence in the percutaneous ablation of primary lesions of the liver with special focus on how AI can impact in the treatment of HCC especially on potential advantages on the drawbacks of the conventional technique.
Indexed as
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Registered trials
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.