Evidence map›Paper›PMID 40386730›Full record

ArticleTranslational lung cancer research2025

Metabolic tumor volume on

Kosuke Hashimoto, Kyoichi Kaira, Atsuto Mouri, Ayako Shiono, Yu Miura, Ou Yamaguchi, Hisao Imai, Hiroshi Kagamu, Ichiei Kuji

Abstract read
In one paragraph

Article in Translational lung cancer research, 2025. 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. 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

9 authors.

Kosuke HashimotoDepartment of Respiratory Medicine, International Medical Center, Saitama Medical University, Saitama, Japan.
Kyoichi KairaDepartment of Respiratory Medicine, International Medical Center, Saitama Medical University, Saitama, Japan.ORCID https://orcid.org/0000-0001-5548-7686
Atsuto MouriDepartment of Respiratory Medicine, International Medical Center, Saitama Medical University, Saitama, Japan.
Ayako ShionoDepartment of Respiratory Medicine, International Medical Center, Saitama Medical University, Saitama, Japan.
Yu MiuraDepartment of Respiratory Medicine, International Medical Center, Saitama Medical University, Saitama, Japan.
Ou YamaguchiDepartment of Respiratory Medicine, International Medical Center, Saitama Medical University, Saitama, Japan.
Hisao ImaiDepartment of Respiratory Medicine, International Medical Center, Saitama Medical University, Saitama, Japan.
Hiroshi KagamuDepartment of Respiratory Medicine, International Medical Center, Saitama Medical University, Saitama, Japan.
Ichiei KujiDepartment of Nuclear Medicine, International Medical Center, Saitama Medical University, Saitama, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Nivolumab plus ipilimumab (Nivo-Ipi) is a standard treatments for metastatic or recurrent non-small cell lung cancer (NSCLC). Unlike programmed death-1 (PD-1) inhibitor monotherapy, the possible predictors of Nivo-Ipi treatment effectiveness remain unclear. Therefore, this retrospective study evaluated the prognostic relevance of 2-deoxy-2-[fluorine-18]-fluoro-d-glucose positron emission tomography ( Methods: Among 130 eligible patients with metastatic or recurrent NSCLC who received Nivo-Ipi as initial treatment and underwent Results: High MTV and TLG on Conclusions: MTV determined based on

Indexed as

2-deoxy-2-[fluorine-18]-fluoro-d-glucose positron emission tomography (18F-FDG PET)Cytotoxic T-lymphocyte-associated antigen 4 (CTLA4)metabolic tumor activitynon-small cell lung cancer (NSCLC)programmed death-1 (PD-1)

Identifiers

PMID40386730
PMCPMC12082181

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.