Evidence map›Paper›PMID 41659713›Full record

ArticleFrontiers in oncology2026

Small-volume plan optimization of inoperable early-stage centrally-located non-small-cell lung cancer using VMAT-based SBRT under the DIBH scenario: a single-arc model or a dual-arc plan?

Yangyang Huang, Jinjin Yuan, Alan Chu, Jun Yang, Yibao Liu

Abstract read
In one paragraph

Article in Frontiers in 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.

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0citing papers in PubMed
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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

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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

5 authors.

Yangyang Huang *Department of Radiotherapy, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Jinjin Yuan *Department of Radiotherapy, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Alan Chu *Department of Radiotherapy, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Jun YangDepartment of Radiotherapy, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Yibao LiuSchool of Nuclear Science and Engineering, East China University of Technology, Nanchang, Jiangxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study aimed to comprehensively analyze the dosimetric parameters, plan complexity, gamma passing rates (GPRs), and most importantly, the beam-on time (BOT) of stereotactic body radiotherapy (SBRT) for small-volume inoperable early-stage centrally-located non-small-cell lung cancer (NSCLC) at a radiotherapy center. The analysis was based on both single-arc (SA) and dual-arc (DA) VMAT techniques under the deep inspiration breath hold (DIBH) scenario. Methods: We retrospectively selected 24 cases of small-volume inoperable early-stage centrally-located NSCLC treated with SBRT under the DIBH scenario at our institution between March 2021 and June 2024. The redesigned SA-VMAT plans (SA plans) adopted the same prescription dose of 50 Gy/5 fractions and flattening-filter free (FFF) beam as the original DA-VMAT plans (DA plans). The 2-group plans (i.e., the SA and DA plans) were normalized to cover 95% of the planning target volume (PTV) and 99% of the gross tumor volume (GTV) by the prescription dose. The evaluation factors included PTV parameters (D Results: The SA technique consistently yielded superior plans. Among the PTV parameters, the SA plans were superior to the DA plans in D Discussion: The SA plans significantly shortened the BOT while maintaining comparable plan quality, thereby improving comfort for patients with small-volume inoperable early-stage centrally located NSCLC under the DIBH scenario. Future studies should accumulate more patient data to evaluate the long-term clinical outcomes of SA plans.

Indexed as

BOTDIBHinoperable early-stage centrally-located NSCLCSA and DA plansSBRTsmall-volumeVMAT

Identifiers

PMID41659713
PMCPMC12875985

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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.