Evidence map›Paper›PMID 39391445›Full record

ArticleCureus2024

A Personalized Anisotropic Margin for Cervical Cancer Radiation Therapy Under the Guidance of Daily Iterative Cone-Beam Computed Tomography (iCBCT).

Haonan Xiao, Qiufen Guo, Junjie Ma, Jian Chen, Peng Xie, Yong Yin

Abstract readCase Reports
PubMed Full text at PMC Publisher
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. Not yet cited in PubMed.

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

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

6 authors.

Haonan XiaoDepartment of Radiation Oncology and Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, CHN.
Qiufen GuoDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, CHN.
Junjie MaDepartment of Radiation Oncology and Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, CHN.
Jian ChenDepartment of Nuclear Science and Technology, School of Nuclear Science and Technology, University of South China, Hengyang, CHN.
Peng XieDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, CHN.
Yong YinDepartment of Radiation Oncology and Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, CHN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Online adaptive radiation therapy (ART) eliminates interfraction uncertainties by adaption before each treatment session. However, intrafraction motions still exist and could become more severe due to long treatment time. Large isotropic margins can ensure clinical target volume (CTV) coverage but at the cost of more organs at risk damage. In this study, we proposed a novel personalized anisotropic margin search algorithm for cervical cancer radiation therapy under the guidance of daily iterative cone-beam computed tomography (iCBCT) to find the optimal margin values for each patient, which achieves the smallest possible planning target volume (PTV) and maintains CTV coverage. Twenty-two online Ethos ART treatment sessions were included for analysis. Two iCBCT scans were taken in each session. The first one (iCBCT

Indexed as

cervical cancerintrafraction motionmotion managementonline artvarian ethos

Identifiers

PMID39391445
PMCPMC11465696

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Registered trials

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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.