Evidence map›Paper›PMID 39703719›Full record

ArticleAdvances in radiation oncology2025

Noninvasive Mechanical Ventilation Is a Promising Way to Improve Lung Cancer Radiation Therapy.

Johannes K Veldman-Landegent, Zdenko van Kesteren, Mike J Parkes, Markus F Stevens, Joost G van den Aardweg, Edith M T Dieleman, Eva Versteijne, Geertjan van Tienhoven, Arjan Bel, Irma W E M van Dijk

Abstract read
In one paragraph

Article in Advances in radiation oncology, 2025. 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

10 authors.

Johannes K Veldman-LandegentDepartment of Radiation Oncology, Amsterdam UMC location Free University, Amsterdam, The Netherlands.
Zdenko van KesterenDepartment of Radiation Oncology, Amsterdam UMC location Free University, Amsterdam, The Netherlands.
Mike J ParkesDepartment of Radiation Oncology, Amsterdam UMC location Free University, Amsterdam, The Netherlands.
Markus F StevensDepartment of Anesthesiology, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands.
Joost G van den AardwegDepartment of Pulmonology, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands.
Edith M T DielemanDepartment of Radiation Oncology, Amsterdam UMC location Free University, Amsterdam, The Netherlands.
Eva VersteijneDepartment of Radiation Oncology, Amsterdam UMC location Free University, Amsterdam, The Netherlands.
Geertjan van TienhovenDepartment of Radiation Oncology, Amsterdam UMC location Free University, Amsterdam, The Netherlands.
Arjan BelDepartment of Radiation Oncology, Amsterdam UMC location Free University, Amsterdam, The Netherlands.
Irma W E M van DijkDepartment of Radiation Oncology, Amsterdam UMC location Free University, Amsterdam, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Accurate radiation therapy (RT) for lung cancer is challenging because of the respiratory motion of the tumor and surrounding organs at risk. Recently, non-invasive mechanical ventilation (NIMV) has been investigated as a novel respiratory motion management strategy. Using NIMV, respiratory motion can be minimized, while a larger lung volume yields less overall lung dose. The purpose of this study was to determine the potential benefit of NIMV to improve lung cancer RT using magnetic resonance imaging (MRI) data of healthy volunteers. Methods and Materials: Twelve healthy volunteers practiced NIMV at 60 breaths per minute (NIMV Results: All volunteers were successfully trained to be ventilated with NIMV Conclusions: NIMV

Identifiers

PMID39703719
PMCPMC11656083

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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.