Evidence map›Paper›PMID 40462878›Full record

ArticleJournal of hepatocellular carcinoma2025

Spleen Volume Dynamics and Survival Outcomes in HCC Patients Undergoing Immune Checkpoint Inhibitors: A Retrospective Analysis.

Xiaona Fu, Shanshan Jiang, Yi Li, Yusheng Guo, Bingxin Gong, Jie Lou, Yanlin Li, Sichen Wang, Yuxin Sun, Yi Ren and 2 more

Abstract read
In one paragraph

Article in Journal of hepatocellular carcinoma, 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. Article
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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

12 authors.

Xiaona Fu *Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People's Republic of China.
Shanshan Jiang *Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People's Republic of China.
Yi Li *Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People's Republic of China.
Yusheng GuoDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People's Republic of China.
Bingxin GongDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People's Republic of China.
Jie LouDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People's Republic of China.
Yanlin LiDepartment of Radiology, the First People's Hospital of Jiangxia District, Wuhan, 430200, People's Republic of China.
Sichen WangSchool of Life Science and Technology, Computational Biology Research Center, Harbin Institute of Technology, Harbin, 150001, People's Republic of China.
Yuxin SunDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People's Republic of China.
Yi RenDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People's Republic of China.
Quan ChenDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People's Republic of China.ORCID 0000-0001-6360-5953
Lian YangDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The spleen serves as an important immune organ which influences the anti-tumor immune response by modulating the immune microenvironment. This study investigated the prognostic impact of spleen volume (SV) on the survival in hepatocellular carcinoma (HCC) patients receiving immune checkpoint inhibitors (ICIs). Patients and Methods: This retrospective study included 224 HCC patients treated with ICIs, categorized into Higher and Lower SV groups by median SV and further into SV increased and Non-SV increased groups based on changes in SV at 3 months after ICIs. Kaplan-Meier curves and Cox regression models were used to evaluate the influence of SV and clinical indicators on progression-free survival (PFS) and overall survival (OS). Independent prognostic factors identified via multivariate analysis were incorporated into nomograms, with their accuracy assessed using concordance index (C-index), time-dependent receiver operating characteristic (ROC) and calibration curves. Restricted cubic spline (RCS) analysis was conducted to assess the relationship between baseline SV and survival. Results: The Higher SV and SV increased groups demonstrated shorter PFS and OS compared to the Lower SV and Non-SV increased groups, respectively. These results were consistent with different regimens in the Child A. The C-index of nomogram for PFS were 0.700 (0.678-0.721) and OS 0.733(0.709-0.757). The ROC and calibration curves confirmed robust discrimination and predictive accuracy of models. RCS analysis revealed a nonlinear association between baseline SV and survival risk, providing a more comprehensive overview of SV in relation to survival in HCC patients treated with ICIs. Conclusion: The baseline SV and its relative change at three months after treatment are expected to become routine imaging makers for predicting survival in HCC patients receiving ICIs, which consequently contributes to their clinical management.

Indexed as

hepatocellular carcinomaimmune checkpoint inhibitorsnomogramspleen volumesurvival

Identifiers

PMID40462878
PMCPMC12132512

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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.