SynthesisCancer medicine2023
Efficacy of fusion imaging for immediate post-ablation assessment of malignant liver neoplasms: A systematic review.
Synthesis in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 4 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 4 syntheses or guidelines pooled it, 60 citations in OpenAlex.
- Artificial intelligence in predicting recurrence after first-line treatment of liver cancer: a systematic review and meta-analysis.BMC medical imaging · 2024Pooled it
- Accuracy of deep learning in the differential diagnosis of coronary artery stenosis: a systematic review and meta-analysis.BMC medical imaging · 2024Pooled it
- Drug-eluting beads transarterial chemoembolization vs conventional transarterial chemoembolization in the treatment of hepatocellular carcinoma in adult patients: a systematic review and update meta-analysis of observational studies.Frontiers in oncology · 2024Pooled it
- Efficacy of fusion imaging for immediate post-ablation assessment of malignant liver neoplasms: A systematic review.Cancer medicine · 2023Pooled it
- Review
- Precision medicine and personalized nursing in cardiovascular disease: clinical applications and frontier developments.Frontiers in cardiovascular medicine · 2026Review
- Article
- Treatment of recurrent hepatocellular carcinoma: The current standards and future perspectives.World journal of gastrointestinal oncology · 2025Review
- Review
- CT-guided Thermal Ablation of Liver Tumors Using Intraprocedural CT-CT Fusion for Applicator Position and Ablation Completeness Assessment: a Single-Center Comparative Analysis.Cardiovascular and interventional radiology · 2025Article
- Surgery or Percutaneous Ablation for Liver Tumors? The Key Points Are: When, Where, and How Large.Cancers · 2025Article
- UGS-M3F: unified gated swin transformer with multi-feature fully fusion for retinal blood vessel segmentation.BMC medical imaging · 2025Article
- Automatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN model.BMC medical imaging · 2025Article
- Microwave ablation for colorectal liver metastases with ultrasound fusion imaging assistance: a stratified analysis study based on tumor size and location.Abdominal radiology (New York) · 2025Article
- GeoCrack: A High-Resolution Dataset For Segmentation of Fracture Edges in Geological Outcrops.Scientific data · 2024Article
- Precise liver tumor ablation: the clinical potential of US-US overlay fusion guidance.Ultrasonography (Seoul, Korea) · 2024Article
- The value evaluation of Nomogram prediction model based on CTA imaging features for selecting treatment methods for isolated superior mesenteric artery dissection.BMC medical imaging · 2024Article
- MRI T2WI-based radiomics combined with KRAS gene mutation constructed models for predicting liver metastasis in rectal cancer.BMC medical imaging · 2024Article
- The Paradox of Modern Technology in Standardizing Thermal Liver Ablation: Fostering Uniformity or Diversity?Cardiovascular and interventional radiology · 2024Article
- Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach.BMC medical imaging · 2024Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPercutaneous thermal ablation has become the preferred therapeutic treatment option for liver cancers that cannot be resected. Since ablative zone tissue changes over time, it becomes challenging to determine therapy effectiveness over an extended period. Thus, an immediate post-procedural evaluation of the ablation zone is crucial, as it could influence the need for a second-look treatment or follow-up plan. Assessing treatment response immediately after ablation is essential to attain favorable outcomes. This study examines the efficacy of image fusion strategies immediately post-ablation in liver neoplasms to determine therapeutic response. METHODOLOGY: A comprehensive systematic search using PRISMA methodology was conducted using EMBASE, MEDLINE (via PUBMED), and Cochrane Library Central Registry electronic databases to identify articles that assessed the immediate post-ablation response in malignant hepatic tumors with fusion imaging (FI) systems. The data were retrieved on relevant clinical characteristics, including population demographics, pre-intervention clinical history, lesion characteristics, and intervention type. For the outcome metrics, variables such as average fusion time, intervention metrics, technical success rate, ablative safety margin, supplementary ablation rate, technical efficacy rate, LTP rates, and reported complications were extracted.
resultsTwenty-two studies were included for review after fulfilling the study eligibility criteria. FI's immediate technical success rate ranged from 81.3% to 100% in 17/22 studies. In 16/22 studies, the ablative safety margin was assessed immediately after ablation. Supplementary ablation was performed in 9 studies following immediate evaluation by FI. In 15/22 studies, the technical effectiveness rates during the first follow-up varied from 89.3% to 100%.
conclusionBased on the studies included, we found that FI can accurately determine the immediate therapeutic response in liver cancer ablation image fusion and could be a feasible intraprocedural tool for determining short-term post-ablation outcomes in unresectable liver neoplasms. There are some technical challenges that limit the widespread adoption of FI techniques. Large-scale randomized trials are warranted to improve on existing protocols. Future research should emphasize improving FI's technological capabilities and clinical applicability to a broader range of tumor types and ablation procedures.
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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.