Evidence map›Paper›PMID 42591139›Full record

ArticleFrontiers in oncology2026

Pre-treatment inflammatory markers can identify the risk of immune-related toxicity in non-small cell lung cancer patients receiving immune checkpoint inhibitor therapy.

XueYuan Zhang, Jia Song

Abstract read
In one paragraph

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

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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

XueYuan ZhangCancer Center, The Fifth Hospital Affiliated to Wenzhou Medical University, Lishui Municipal Central Hospital, Lishui, Zhejiang, China.
Jia SongCancer Center, The Fifth Hospital Affiliated to Wenzhou Medical University, Lishui Municipal Central Hospital, Lishui, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This work intended to explore how peripheral blood inflammatory markers at baseline predict the occurrence of immune-related adverse events (irAEs) in patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs). Methods: This retrospective study included 285 patients with stage III-IV NSCLC who received ICI therapy at our hospital from January 2020 to December 2023. Based on the presence of irAEs, patients were assigned to an irAEs group (91 patients) or a non-irAEs group (194 patients). Baseline clinical data and peripheral blood inflammatory markers (including CRP, TNF-α, IL-2, IL-4, IL-6, NLR, MLR, PLR, and SII) were collected within one week before treatment initiation. LASSO regression was applied for variable selection, followed by multivariate logistic regression to identify independent predictors of irAEs. A nomogram prediction model was constructed and its discrimination, calibration, and clinical utility were assessed using AUC, calibration curves, and decision curve analysis, with internal validation via bootstrap resampling. Results: 91 patients (31.93%) developed irAEs, with the most common being pneumonia (21 cases). OS and PFS did not significantly differ between the irAEs and non-irAEs groups, according to survival analysis ( Conclusion: The nomogram model, based on pre-treatment peripheral blood inflammatory markers (CRP, IL-4, NLR, IL-2, and TNF-α), demonstrated good discriminatory and calibration capabilities in the validation cohort. However, this model should be regarded as an exploratory tool designed to generate hypotheses for identifying patients at risk of developing immune-related adverse events. External validation is required before any clinical application.

Indexed as

immune checkpoint inhibitorsimmune-related adverse eventsinflammatory markersnomogramnon-small cell lung cancer

Identifiers

PMID42591139
PMCPMC13461320

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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.