Evidence map›Paper›PMID 34312690›Full record

ArticleCardiovascular and interventional radiology2021

Utility of Real-time CT/MRI-US Automatic Fusion System Based on Vascular Matching in Percutaneous Radiofrequency Ablation for Hepatocellular Carcinomas: A Prospective Study.

Seungchul Han, Jeong Min Lee, Dong Ho Lee, Jeong Hee Yoon, Won Chang

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Article in Cardiovascular and interventional radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

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0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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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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3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Seungchul HanDepartment of Radiology, Seoul National University Hospital, #101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
Jeong Min LeeDepartment of Radiology, Seoul National University Hospital, #101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. jmsh@snu.ac.kr.
Dong Ho LeeDepartment of Radiology, Seoul National University Hospital, #101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
Jeong Hee YoonDepartment of Radiology, Seoul National University Hospital, #101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
Won ChangDepartment of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo prospectively evaluate the technical success rate of real-time computed tomography/magnetic resonance imaging and ultrasound (CT/MRI-US) automatic fusion system and the long-term therapeutic efficacy of radiofrequency ablation (RFA) guided by automatic fusion in hepatocellular carcinoma (HCC) patients. MATERIALS AND

methods139 patients with 151 HCCs were prospectively enrolled for RFA guided by an automatic CT/MRI-US fusion system (PercuNav system, Philips, the Netherlands). Automatic fusion imaging, based on vascular segmentation and registration, was performed by sonographic sweeping at the intercostal plane. The fusion quality, tumor localization confidence and technical feasibility were recorded before and after fusion using a scoring system. Technical success rate of the RFA procedure and local tumor progression (LTP) were assessed during follow-up. Analysis of technical success and LTP was performed using generalized estimating equations and Cox proportional hazard regression analysis.

resultsThe success rate of the fusion system was 82.7% (115/139) per patient. The mean sonographic scan time for fusion was 154.4 ± 108.4 s. In patients with successful fusion, the score indicating tumor localization confidence (2.2 ± 0.8 vs. 2.7 ± 0.9) and technical feasibility (2.6 ± 0.8 vs. 3.4 ± 0.7) increased after fusion (p < 0.001). The technical success rate of the RFA procedure was 96.8% (120/124) per tumor in patients with successful fusion, including poorly localized tumors. LTP rates were 8.6%, 12.2% and 15.2% at 1, 2 and 3 years.

conclusionThe CT/MRI-US automatic fusion system showed a high success rate for image registration and facilitated better feasibility and a high technical success rate of RFA in HCCs, even with poor localization on US. LEVEL OF EVIDENCE: Level 3b, Nonrandomized prospective study.

Indexed as

Carcinoma, HepatocellularCatheter AblationLiver NeoplasmsRadiofrequency AblationHumansMagnetic Resonance ImagingProspective StudiesRetrospective StudiesTomography, X-Ray ComputedTreatment OutcomeCTHepatocellular carcinomaMRI-US automatic fusion systemRadiofrequency ablation

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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.