Evidence map›Paper›PMID 42047297›Full record

SynthesisCancer medicine2026

Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Chinese Retrospective Multicenter Study Based on Global Meta-Analysis.

Jia Xu, Yue-Lu Zhang, Man Yang, Zhengbo Deng, Laisheng Li, Ting Wang, Jinhua Song, Hai Xu, Jian Wu, Zhi-Qi Wu and 1 more

Abstract readMeta-AnalysisMulticenter Study
In one paragraph

Synthesis in Cancer medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Jia XuDepartment of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Yue-Lu ZhangDepartment of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Man YangYancheng TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Yancheng, China.
Zhengbo DengChangshu Hospital Affiliated to Nanjing University of Chinese Medicine, Changshu, China.
Laisheng LiDepartment of Laboratory Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.ORCID https://orcid.org/0000-0001-9336-8397
Ting WangDepartment of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.ORCID https://orcid.org/0000-0001-9523-8019
Jinhua SongHepatobiliary Center, Key Laboratory of Liver Transplantation, Chinese Academy of Medical Sciences, NHC Key Laboratory of Living Donor Liver Transplantation, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Hai XuDepartment of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China.ORCID https://orcid.org/0000-0002-5664-6789
Jian WuCenter of Hepato-Pancreato-Biliary Surgery, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, China.
Zhi-Qi WuDepartment of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Hua-Guo XuDepartment of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.ORCID https://orcid.org/0000-0001-7170-9445

Funding

Jiangsu Provincial Medical Key Discipline (Laboratory) ZDXK202239Jiangsu Provincial Research Hospital YJXYY202201Natural Science Foundation of Jiangsu Province of China BK20181492
6 · The paper itself

Abstract

objectiveMicrovascular invasion (MVI) has been identified as a risk factor for the prognosis of patients with hepatocellular carcinoma (HCC). However, it can only be diagnosed pathologically, and thus no widely applicable preoperative MVI risk prediction model has been established. The aim of this study was to develop a preoperative predictive model for MVI.

designThe model derivation set was derived from a global meta-analysis involving 39,253 patients. Model validation was performed at the First Affiliated Hospital of Nanjing Medical University (Nanjing set), which included 538 participants, and the First Affiliated Hospital of Sun Yat-sen University (Guangzhou set), which included 111 participants. The model's discriminative ability, assessed by the concordance index (C-statistic), and calibration curves for MVI probability were evaluated.

resultsThrough meta-analysis, we identified 40 MVI risk factors and utilized the three most widely used preoperative indicators (alpha-fetoprotein [AFP], tumor size, and tumor margin) to establish an MVI risk prediction model. The C-statistics of the scoring model were 0.805 (95% CI 0.765-0.844) in the Nanjing set and 0.808 (95% CI 0.729-0.887) in the Guangzhou set. Calibration of the established preoperative MVI risk prediction model was adequate (all χ

conclusionThe preoperative MVI risk prediction model developed via global meta-analysis exhibits good discriminative ability in different Chinese validation cohorts. Preoperative assessment of MVI status can guide the selection of hepatectomy type, surgical margin width, and neoadjuvant therapy administration, thereby ultimately improving patient prognosis.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsMicrovesselsAdultAgedalpha-FetoproteinsChinaEast Asian PeopleFemaleHumansMaleMiddle AgedNeoplasm InvasivenessPreoperative PeriodPrognosisRetrospective Studiesalpha-Fetoproteinshepatocellular carcinomamicrovascular invasionpreoperative paramentsrisk prediction

Identifiers

PMID42047297
PMCPMC13122567

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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.