Evidence map›Paper›PMID 39430341›Full record

ArticleTranslational lung cancer research2024

Lymph nodes rather than pleural metabolic activity in

Yuxin Jiang, Tao Liu, Ke Xu, Qinpei Cheng, Wanjun Lu, Jingyuan Xie, Mo Chen, Yu Li, Yanjun Du, Shuo Liang and 4 more

Abstract read
In one paragraph

Article in Translational lung cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

14 authors.

Yuxin JiangSchool of Medicine, Southeast University, Nanjing, China.
Tao LiuDepartment of Nuclear Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Ke XuDepartment of Respiratory and Critical Care Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Qinpei ChengDepartment of Respiratory and Critical Care Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Wanjun LuDepartment of Respiratory and Critical Care Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Jingyuan XieDepartment of Respiratory and Critical Care Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Mo ChenDepartment of Respiratory and Critical Care Medicine, Jinling Hospital, Affiliated Hospital of Nanjing Medical School, Nanjing, China.
Yu LiDepartment of Respiratory and Critical Care Medicine, Jinling Hospital, Affiliated Hospital of Nanjing Medical School, Nanjing, China.
Yanjun DuDepartment of Respiratory and Critical Care Medicine, Jinling Hospital, Affiliated Hospital of Nanjing Medical School, Nanjing, China.
Shuo LiangDepartment of Respiratory and Critical Care Medicine, Jinling Hospital, Affiliated Hospital of Nanjing Medical School, Nanjing, China.
Yong SongSchool of Medicine, Southeast University, Nanjing, China.
Jiang WuDepartment of Nuclear Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Tangfeng LvSchool of Medicine, Southeast University, Nanjing, China.
Ping ZhanSchool of Medicine, Southeast University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Frequently recurrent malignant pleural effusion (MPE) significantly hampers the life quality of advanced non-small cell lung cancer (NSCLC) patients. We aimed to explore the effects of progression patterns and local intervention on MPE recurrence and apply fluorodeoxyglucose positron emission tomography/computed tomography ( Methods: We retrospectively recruited two cohorts of patients including treatment-naïve NSCLC diagnosed with MPE at the onset and receiving PET/CT scanning, as well as those with MPE and undergoing first-line epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) treatment. Pleural maximum standardized uptake value (SUV Results: A total of 148 treatment-naïve patients with EGFR-TKI treatment and MPE were recruited during the median follow-up period of 683 days, with 69 (46.6%) and 35 (23.6%) witnessing MPE recurrence at least once and twice. Intrapleural perfusion therapy at first recurrence was a protective factor for the second MPE recurrence (P=0.006), while intrapleural perfusion therapy at baseline could not benefit the first MPE recurrence (P=0.14). Conversely, prior systemic progression indicative of the change of systemic treatment was a protective factor for time to the first MPE recurrence (P<0.001); instead, the change of systemic treatment at the first MPE recurrence was not associated with second MPE recurrence (P=0.53). In another cohort with treatment-naïve NSCLC patients with MPE and PET/CT scanning, 103 patients regardless of the actionable mutation status were recruited during the median follow-up period of 304 days. Multivariate analysis suggested that the LN SUV Conclusions: In summary, LN rather than pleural metabolic activity or uptake patterns could predict MPE recurrence for patients with or without targeted therapy. We should re-consider the application of intrapleural perfusion treatment for first-onset MPE and prompt it more at the moment of recurrent MPE. Promisingly, we could probably apply the non-invasive tool to identify the risk factors for MPE recurrence.

Indexed as

malignant pleural effusion (MPE)non-small cell lung cancer (NSCLC)Positron emission tomography/computed tomography (PET/CT)recurrencetargeted therapy

Identifiers

PMID39430341
PMCPMC11484712

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.