Evidence map›Paper›PMID 41212769›Full record

ArticleThe oncologist2025

An early relapse prediction model based on pathological features following neoadjuvant immunotherapy for hepatocellular carcinoma.

Yanrui Pang, Xuelian Zhao, Xinxin Guo, Jing Han, Yuan Ji

Abstract read
In one paragraph

Article in The oncologist, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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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.

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

1 citing paper in PubMed.

  1. 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

5 authors.

Yanrui PangDepartment of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China.
Xuelian ZhaoDepartment of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China.
Xinxin GuoDepartment of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China.
Jing HanDepartment of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China.
Yuan JiDepartment of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China.

Funding

Research Projects from the Science and Technology Commission of Shanghai Municipality 22JC1403002
6 · The paper itself

Abstract

backgroundThe emergence of neoadjuvant immunotherapy has improved outcomes for hepatocellular carcinoma (HCC) patients, yet early postoperative recurrence remains a critical challenge. This study aimed to identify clinicopathological and immune microenvironment features associated with recurrence-free survival (RFS) and develop a predictive model for early recurrence in HCC patients undergoing neoadjuvant anti-PD-1 antibody therapy. MATERIALS AND

methodsClinicopathological characteristics and immune microenvironment profiles were analyzed in 70 HCC patients treated with neoadjuvant anti-PD-1 antibody and 20 patients receiving transarterial chemoembolization (TACE). Key variables, including microvascular invasion (MVI), tumor capsule integrity, and immune cell infiltration, were evaluated. Statistical analyses included multivariate Cox regression, Kaplan-Meier survival analysis, and nomogram construction with internal validation to predict recurrence risk.

resultsFoam cell response and tumor-infiltrating lymphocytes (TILs) were strongly linked to favorable immunotherapy responses. Patients without MVI, those with intact tumor capsules, and those exhibiting high CD4+ T cell density in central tumor regions demonstrated significantly prolonged RFS. A nomogram integrating these three factors achieved robust predictive accuracy for early recurrence stratifying patients into distinct risk groups.

conclusionsThis study highlights MVI absence, tumor capsule integrity, and CD4+ T cell infiltration as key predictors of RFS in HCC patients receiving neoadjuvant immunotherapy. The proposed nomogram provides a clinically actionable tool for early recurrence risk assessment, enabling personalized postoperative monitoring and adjuvant therapy strategies to improve survival outcomes.

Indexed as

Carcinoma, HepatocellularImmunotherapyLiver NeoplasmsNeoadjuvant TherapyNeoplasm Recurrence, LocalAgedChemoembolization, TherapeuticFemaleHumansMaleMiddle AgedNomogramsPrognosisTumor Microenvironmenthepatocellular carcinomaimmunotherapyneoadjuvantnomogramrecurrence

Identifiers

PMID41212769
PMCPMC12771520

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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.