Evidence map›Paper›PMID 42013549›Full record

ArticleTranslational oncology2026

Inflammatory biomarkers refine progression risk stratification in NSCLC patients with stable disease.

Markus Kleinberger, Severin Laengle, Julia Maria Berger, Lynn Gottmann, Vincent Sunder-Plassmann, Martin Korpan, Isabella Solano Henao, Josef Fuerst, Angelika Martina Starzer, Luzia Berchtold and 4 more

Abstract read
In one paragraph

Article in Translational 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
–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

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

14 authors.

Markus KleinbergerDepartment of Medicine I, Division of Oncology, Medical University of Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Austria.
Severin LaengleDepartment of Medicine I, Division of Oncology, Medical University of Vienna, Austria.
Julia Maria BergerDepartment of Medicine I, Division of Oncology, Medical University of Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Austria.
Lynn GottmannDepartment of Medicine I, Division of Oncology, Medical University of Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Austria.
Vincent Sunder-PlassmannDepartment of Medicine I, Division of Oncology, Medical University of Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Austria.
Martin KorpanDepartment of Medicine I, Division of Oncology, Medical University of Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Austria.
Isabella Solano HenaoDepartment of Medicine I, Division of Oncology, Medical University of Vienna, Austria.
Josef FuerstDepartment of Medicine I, Division of Oncology, Medical University of Vienna, Austria.
Angelika Martina StarzerDepartment of Medicine I, Division of Oncology, Medical University of Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Austria.
Luzia BerchtoldDepartment of Medicine I, Division of Oncology, Medical University of Vienna, Austria; Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Austria.
Erwin TomasichDepartment of Medicine I, Division of Oncology, Medical University of Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Austria.
Matthias PreusserDepartment of Medicine I, Division of Oncology, Medical University of Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Austria.
Julia FurtnerDepartment of Biomedical imaging and Image-guided Therapy, Division of General and Paediatric Radiology, Medical University of Vienna, Austria.
Anna Sophie BerghoffDepartment of Medicine I, Division of Oncology, Medical University of Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Austria. Electronic address: anna.berghoff@meduniwien.ac.at.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionEarly risk stratification of non-small cell lung cancer (NSCLC) patients with stable disease (SD) at first restaging is particularly challenging. We explored the prognostic value of clinical and inflammatory markers in this population. METHODS AND MATERIAL: We analysed a real-world cohort of prospectively enrolled advanced NSCLC patients undergoing systemic intravenous anticancer treatment in a palliative intent at the Medical University of Vienna between 2019 and 2024. Inflammatory blood markers were measured at baseline and first restaging, with blinded radiologic assessment. Uni- and multivariable logistic regression models evaluated associations with durable clinical benefit (DCB).

resultsEighty NSCLC patients with SD at first restaging were included (median age 65 years, 50% female). Of those, 41 (51.3%) achieved DCB. Baseline characteristics were largely comparable. Patients with DCB had lower baseline neutrophil-to-lymphocyte and lymphocyte-to-leukocyte ratios. At first follow-up, CRP was lower and albumin higher in patients with DCB. In univariable analysis, lower follow-up albumin and higher LDH were associated with reduced odds of DCB. In multivariable models, PD-L1 positivity and follow-up albumin remained associated with DCB. The combined clinical-inflammatory model showed the highest apparent discriminative performance (AUC 0.766), compared to clinical-only (AUC 0.657) and inflammatory-only models (AUC 0.727), although the incremental improvement was modest. DISCUSSION: In patients with advanced NSCLC and SD at first restaging, inflammatory biomarkers were associated with additional discriminative information beyond clinical characteristics alone. A combined clinical-inflammatory model showed numerically higher discriminative performance; however, the improvement was modest.

Indexed as

BiomarkerLung cancerRadiological imagingRECISTStable disease

Identifiers

PMID42013549
PMCPMC13123324

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.