Evidence map›Paper›PMID 39360108›Full record

ArticleCureus2024

Large Pleural Metastases With Significant Inter-fractional Volume Reduction During Online Adaptive Radiotherapy: A Case Report With Dosimetry Comparison.

Yu-Rou Chiou, Ting Chun Lin, Jin-Huei Ji, An-Cheng Shiau, Chi-Hsien Huang, Ji-An Liang

Abstract readCase Reports
In one paragraph

Article in Cureus, 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

6 authors.

Yu-Rou ChiouDepartment of Radiation Oncology, China Medical University Hospital, Taichung City, TWN.
Ting Chun LinDepartment of Radiation Oncology, China Medical University Hospital, Taichung City, TWN.
Jin-Huei JiDepartment of Radiation Oncology, China Medical University Hospital, Taichung City, TWN.
An-Cheng ShiauDepartment of Radiation Oncology, China Medical University Hospital, Taichung City, TWN.
Chi-Hsien HuangDepartment of Radiation Oncology, China Medical University Hospital, Taichung City, TWN.
Ji-An LiangDepartment of Radiation Oncology, China Medical University Hospital, Taichung City, TWN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Online adaptive radiotherapy (oART) dose calculation relies on synthetic computed tomography (sCT), which notably influences anatomical changes. This study elucidates how sCT may respond to significant inter-fractional tumor volume reduction and its subsequent impact on dose distribution. In this case report, we exported sCT and cone-beam CT (CBCT) images from each treatment session. We retrospectively analyzed 20 adaptive and scheduled plans of a patient receiving oART for large pleural metastases with notable inter-fractional tumor regression. By overriding the CT number of the dissipated tumor volume with that of the lungs on each sCT, we recalculated each plan. We compared the dose distribution between the adaptive and scheduled plans. Percentage dose difference and 3D gamma analysis were employed to assess dose variability. Results of the dose analysis showed that, compared to the online (non-overridden) plans, the recalculated plans using overridden sCT demonstrated right-shifted dose-volume histogram curves for the targets and right lung, with a slight but statistically significant increase of no less than 1.5% in

Indexed as

cbctdose uncertaintyethos platforminter-fractional tumor volumeonline adaptive radiotherapyplan evaluationpleural metastasessynthetic cttreatment planningtumor volume reduction

Identifiers

PMID39360108
PMCPMC11445199

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.