Evidence map›Paper›PMID 40718485›Full record

ReviewFrontiers in immunology2025

Multiplex imaging analysis of the tumor immune microenvironment for guiding precision immunotherapy.

Feng Liu, Guiqing Li, Yi Zheng, Yanxia Liu, Kang Liu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–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

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Targeting the Barriers Driving Immune Exclusion.Pharmaceuticals (Basel, Switzerland) · 2026
    Review
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  7. Article
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  10. Tumorigenesis and Tumor Microenvironment in Lung Cancer.Current issues in molecular biology · 2026
    Review
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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

5 authors.

Feng LiuFaculty of Medical Images, Shandong Second Medical University, Weifang, Shandong, China.
Guiqing LiDepartment of Oncology, Afiliated Hospital of Shandong Second Medical University, Weifang, Shandong, China.
Yi ZhengDepartment of Oncology, Afiliated Hospital of Shandong Second Medical University, Weifang, Shandong, China.
Yanxia LiuDepartment of Oncology, Afiliated Hospital of Shandong Second Medical University, Weifang, Shandong, China.
Kang LiuDepartment of Oncology, Afiliated Hospital of Shandong Second Medical University, Weifang, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cutting-edge multiplex imaging technologies have significantly advanced our understanding of the tumor immune microenvironment (TIME), delivering nanometer-scale spatial resolution that illuminates previously inaccessible cellular interactions and organizational patterns. This mini-review discusses the latest multiplex imaging methods, including Imaging Mass Cytometry (IMC), Multiplexed Ion Beam Imaging (MIBI), Cyclic Immunofluorescence (CycIF), and Digital Spatial Profiling (DSP), emphasizing their roles in identifying spatial immune signatures predictive of immunotherapy responses. Clinical applications across various cancers-such as NSCLC, melanoma, breast cancer, colorectal cancer, and hepatocellular carcinoma-highlight how spatially resolved immune profiles can enhance patient stratification and treatment personalization. Notably, increased CD8

Indexed as

ImmunotherapyNeoplasmsPrecision MedicineTumor MicroenvironmentAnimalsHumansimmune cell interactionsimmunotherapy biomarkersmultiplex imagingspatial profilingtumor immune microenvironment (TIME)

Identifiers

PMID40718485
PMCPMC12289683

What OpenQuestion holds

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LicenceCC BY
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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.