Evidence map›Paper›PMID 41047877›Full record

SynthesisAnnals of medicine2025

Artificial intelligence for the prediction of posthepatectomy recurrence in hepatocellular carcinoma: a systematic review and meta-analysis.

Zhiqiang Xiang, Jing Deng, Hao Liang, Mengliang Jiang, Yuhan Liang, Zhaohai Liu, Yachen Wu, Leyuan Peng, Xiaoming Dai, Zhu Zhu

Abstract readSystematic ReviewMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
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

10 authors.

Zhiqiang XiangDepartment of Hepatobiliary Surgery, Hunan University of Medicine General Hospital, Huaihua, China.
Jing DengDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China.
Hao LiangThe First Affiliated Hospital, Department of Hepatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, China.
Mengliang JiangThe First Affiliated Hospital, Department of Hepatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, China.
Yuhan LiangCollege of Medical Imaging Laboratory and Rehabilitation, Xiangnan University, Chenzhou, China.
Zhaohai LiuThe First Affiliated Hospital, Department of Hepatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, China.
Yachen WuThe First Affiliated Hospital, Department of Hepatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, China.
Leyuan PengDepartment of Hepatobiliary Surgery, Hunan University of Medicine General Hospital, Huaihua, China.
Xiaoming DaiThe First Affiliated Hospital, Department of Hepatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, China.
Zhu ZhuThe First Affiliated Hospital, Department of Hepatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, China.ORCID 0000-0003-0274-8007

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivePosthepatectomy recurrence of hepatocellular carcinoma (HCC) is a major cause of poor prognosis. Accurate prediction is essential for reducing the burden of advanced disease and improving outcomes.

methodsA systematic search of the PubMed, Embase, and Cochrane Library databases was conducted from their inception to December 31, 2024. The standard quality assessment of diagnostic accuracy studies (QUADAS-2) tool was utilized to analyse the methodological quality of the included studies. Bivariate linear mixed models were used to pool diagnostic estimates, including sensitivity (Se), specificity (Sp), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). Additionally, the area under the receiver operating characteristic curves (AUC) of the included studies was utilized to evaluate the diagnostic value.

resultsA total of 6665 HCC patients included in 20 studies were enrolled. The pooled Se, Sp, PLR, NLR, DOR and AUC for the overall AI-assisted diagnostic performance for postoperative HCC recurrence were 0.87 (95% CI: 0.72-0.83), 0.85 (95% CI: 0.80-0.90), 5.39 (95% CI: 3.85-7.55), 0.25 (95% CI: 0.20-0.33), 21 (95% CI: 13-35), and 0.89 (95% CI: 0.86-0.91), respectively.

conclusionAI showed high accuracy in predicting the posthepatectomy recurrence of HCC and would shed light on screening and monitoring high-risk patients following liver resection for further treatment.

Indexed as

Artificial IntelligenceCarcinoma, HepatocellularHepatectomyLiver NeoplasmsNeoplasm Recurrence, LocalHumansPrognosisROC Curveartificial intelligenceHepatocellular carcinomameta-analysispredictionrecurrence

Identifiers

PMID41047877
PMCPMC12502119

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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.