Evidence map›Paper›PMID 39958820›Full record

ArticleFrontiers in medicine2025

Baseline

Lu Zheng, Yanzhu Bian, Yujing Hu, Congna Tian, Xinchao Zhang, Shuheng Li, Xin Yang, Yanan Qin

Abstract read
In one paragraph

Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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

  1. Pooled it
  2. 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

8 authors.

Lu ZhengDepartment of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, China.
Yanzhu BianDepartment of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, China.
Yujing HuDepartment of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, China.
Congna TianDepartment of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, China.
Xinchao ZhangDepartment of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, China.
Shuheng LiDepartment of Nuclear Medicine, Affiliated Hospital of Hebei University, Baoding, China.
Xin YangDepartment of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, China.
Yanan QinDepartment of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To analyse positron emission tomography/ computed tomography (PET/CT) imaging and clinical data from patients with non-small cell lung cancer (NSCLC), to identify characteristics of survival beneficiaries of immune checkpoint inhibitors (ICIs) treatment and to establish a survival prediction model. Methods: A retrospective analysis was conducted on PET/CT imaging and clinical parameters of 155 NSCLC patients who underwent baseline PET/CT examination at the Department of Nuclear Medicine, Hebei General Hospital. The Kaplan-Meier curve was employed to compare progression-free survival (PFS) and overall survival (OS) between the ICIs and non-ICIs group and to assess the impact of variables on PFS and OS in the ICIs group. Multivariate Cox proportional hazards regression analysis was conducted with parameters significantly associated with survival in univariate analysis. Results: Significant differences were observed in PFS ( Conclusion: Smoking history, primary lesion SUVmax, MTVp, and TLGp were independent predictors of PFS, whilst age, smoking history, SUVmax, MTVp, and TLGp were independent predictors of OS in the ICIs group. Patients without a history of smoking and with SUVmax ≤19.2, MTVp ≤20.745cm

Indexed as

immune checkpoint inhibitorsnon-small cell lung cancerPET/CTprognosisSUV

Identifiers

PMID39958820
PMCPMC11825783

What OpenQuestion holds

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Registered trials

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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.