Evidence map›Paper›PMID 34815974›Full record

ArticleJournal of hepatocellular carcinoma2021

An Optimal Ablative Margin of Small Single Hepatocellular Carcinoma Treated with Image-Guided Percutaneous Thermal Ablation and Local Recurrence Prediction Base on the Ablative Margin: A Multicenter Study.

Feng-Yao Li, Jian-Guo Li, Song-Song Wu, Huo-Lin Ye, Xu-Qi He, Qing-Jing Zeng, Rong-Qin Zheng, Chao An, Kai Li

Open access · goldAbstract read
In one paragraph

Article in Journal of hepatocellular carcinoma, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.5field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 17 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Feng-Yao Li *Department of Ultrasound, Guangdong Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China.
Jian-Guo Li *The Department of Infectious Disease,The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China.ORCID 0000-0002-9406-7921
Song-Song WuShengli Clinical Medical College of Fujian Medical University, Department of Ultrasonography,Fujian Provincial Hospital, Fuzhou, People's Republic of China.
Huo-Lin YeDepartment of Ultrasound, Guangdong Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China.
Xu-Qi HeDepartment of Ultrasound, Guangdong Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China.ORCID 0000-0001-5426-6769
Qing-Jing ZengDepartment of Ultrasound, Guangdong Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China.
Rong-Qin ZhengDepartment of Ultrasound, Guangdong Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China.
Chao AnDepartment of Minimal Invasive Intervention, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.
Kai LiDepartment of Ultrasound, Guangdong Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China.
Sun Yat-sen University · CNFujian Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo explore the best ablative margin (AM) for single hepatocellular carcinoma (HCC) patients with image-guided percutaneous thermal ablation (IPTA) based on MRI-MRI fusion imaging, and to develop and validate a local tumor progression (LTP) predictive model based on the recommended AM.

methodsBetween March 2014 and August 2019, 444 treatment-naïve patients with single HCC (diameter ≤3 cm) who underwent IPTA as first-line treatment from three hospitals were included, which were randomly divided into training (n= 296) and validation (n = 148) cohorts. We measured the ablative margin (AM) by MRI-MRI fusion imaging based on pre-ablation and post-ablation images. Then, we followed up their LPT and verified the optimal AM. Risk factors related to LTP were explored through Cox regression models, the nomogram was developed to predict the LTP risk base on the risk factors, and subsequently validated. The predictive performance and discrimination were assessed and compared with conventional indices.

resultsThe median follow-up was 19.9 months (95% CI 18.0-21.8) for the entire cohort. The results revealed that the tumor size (HR: 2.16; 95% CI 1.25-3.72; P = 0.003) and AM (HR: 0.72; 95% CI, 0.61-0.85; P < 0.001) were independent prognostic factors for LTP. The AM had a pronounced nonlinear impact on LTP, and a cut-off value of 5-mm was optimal. We developed and validated an LTP predictive model based on the linear tumor size and nonlinear AM. The model showed good predictive accuracy and discrimination (training set, concordance index [C-index] of 0.751; validation set, C-index of 0.756) and outperformed other conventional indices.

conclusionThe 5-mm AM is recommended for the best IPTA candidates with single HCC (diameter ≤3 cm). We provided an LTP predictive model that exhibited adequate performance for individualized prediction and risk stratification.

Indexed as

ablative marginhepatocellular carcinomalocal tumor progressionprediction modelthermal ablation

Identifiers

PMID34815974
PMCPMC8604653
OpenAlexW3214610773

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.