Evidence map›Paper›PMID 40699302›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2026

Using

Zewen Jiang, David Haberl, Clemens Spielvogel, Szabolcs Szakall, Peter Molnar, Josef Yu, Victor Lungu, Janos Fillinger, Ferenc Renyi-Vamos, Clemens Aigner and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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

2 citing papers in PubMed.

  1. Article
  2. SUVmax onBioMed research international · 2026
    Article
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

15 authors.

Zewen JiangDepartment of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
David HaberlDepartment of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
Clemens SpielvogelDepartment of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
Szabolcs SzakallPozitron PET/CT Center, Budapest, Hungary.
Peter MolnarPozitron PET/CT Center, Budapest, Hungary.
Josef YuDepartment of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
Victor LunguNational Koranyi Institute of Pulmonology, Budapest, Hungary.
Janos FillingerNational Koranyi Institute of Pulmonology, Budapest, Hungary.
Ferenc Renyi-VamosNational Koranyi Institute of Pulmonology, Budapest, Hungary.
Clemens AignerDepartment of Thoracic Surgery, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria.
Balazs DomeNational Koranyi Institute of Pulmonology, Budapest, Hungary.
Christian LangDepartment of Thoracic Surgery, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria.
Lukas KennerChristian Doppler Laboratory of Applied Metabolomics, Medical University of Vienna, Vienna, Austria.
Zsolt MegyesfalviNational Koranyi Institute of Pulmonology, Budapest, Hungary.
Marcus HackerDepartment of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria. marcus.hacker@meduniwien.ac.at.ORCID 0000-0002-4222-4083

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lymphovascular invasion (LVI) in non-small cell lung cancer (NSCLC) is a critical prognostic marker linked to higher risks of metastasis and recurrence. This study aimed to develop a non-invasive predictive model using body composition features from

methodsWe retrospectively analyzed 248 patients, including 153 from Vienna (training cohort) and 95 from Budapest (validation cohort). Preoperative

resultsLVI was present in 66/153 (43.1%) of Vienna and 39/95 (41.1%) of Budapest patients. The nomogram, developed using the Vienna training cohort, incorporating MTV, N stage, and body composition achieved an AUC of 0.839 and 0.790 in the Budapest validation cohort. Statistical tests confirmed that the nomogram significantly outperformed models based on either clinical (p = 7.92e-06) or imaging variables alone (p = 0.0474). Furthermore, LVI predicted by the nomogram was associated with significantly poorer 3-year recurrence-free and 5-year survival.

conclusionIntegrating body composition with clinical and tumor metabolic features from PET/CT enables preoperative prediction of LVI in NSCLC, supporting improved risk stratification.

Indexed as

Body CompositionCarcinoma, Non-Small-Cell LungFluorodeoxyglucose F18Lung NeoplasmsPositron Emission Tomography Computed TomographyAdultAgedFemaleHumansLymphatic MetastasisMaleMiddle AgedNeoplasm InvasivenessNomogramsRetrospective StudiesFluorodeoxyglucose F1818F-FDG PET/CTBody compositionLymphovascular invasionNSCLC

Identifiers

PMID40699302
PMCPMC12830411

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.