Evidence map›Paper›PMID 33002387›Full record

ArticleThe British journal of radiology2020

Comparison of virtual non-contrast dual-energy CT and a true non-contrast CT for contouring in radiotherapy of 3D printed lung tumour models in motion: a phantom study.

Dominik Alexander Hering, Kai Kröger, Ralf W Bauer, Hans Theodor Eich, Uwe Haverkamp

Abstract readComparative Study
In one paragraph

Article in The British journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Dual-Energy CT in Oncologic Imaging.Tomography (Ann Arbor, Mich.) · 2024
    Review
  3. Article
  4. Article
  5. 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

5 authors.

Dominik Alexander HeringDepartment of Radiation Oncology, University Hospital of Muenster, Muenster, Germany.ORCID https://orcid.org/0000-0002-5131-1859
Kai KrögerDepartment of Radiation Oncology, University Hospital of Muenster, Muenster, Germany.
Ralf W BauerRNS, Private Radiology and Radiation Therapy Group, Wiesbaden, Germany.
Hans Theodor EichDepartment of Radiation Oncology, University Hospital of Muenster, Muenster, Germany.
Uwe HaverkampDepartment of Radiation Oncology, University Hospital of Muenster, Muenster, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis work aims to investigate whether virtual non-contrast (VNC) dual-energy CT(DECT) of contrasted lung tumours can be used as an alternative for true non-contrast (TNC) images in radiotherapy. Two DECT techniques and a TNC CT were compared and influences on gross tumour volume (GTV) volume and CT number from motion artefacts in three-dimensional printed lung tumour models (LTM) in amotion phantom were examined.

methodsTwo spherical LTMs (diameter 3.0 cm) with different inner shapes were created in a three-dimensional printer. The inner shapes contained water or iodine (concentration 5 mg ml

resultsDeviations in GTV volume (outer shape) of LTMs in motion for contrast-enhanced ss-DECT and ds-DECT VNC images compared to TNC images are not significant (

conclusionss-DECT VNC images could not sufficiently subtract iodine from water in LTMs inmotion, whereas ds-DECT VNC images might be a valid alternative to a TNC CT. ADVANCES IN KNOWLEDGE: ds-DECT provides a contrasted image for contouring and a non-contrasted image for radiotherapy treatment planning for LTM in motion.

Indexed as

Models, AnatomicPhantoms, ImagingPrinting, Three-DimensionalContrast MediaLung NeoplasmsMotionTomography, X-Ray ComputedTumor BurdenContrast Media

Identifiers

PMID33002387
PMCPMC7715995

What OpenQuestion holds

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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.