Evidence map›Paper›PMID 37670397›Full record

ArticleRadiation oncology (London, England)2023

Comparison of four synthetic CT generators for brain and prostate MR-only workflow in radiotherapy.

Damien Autret, Camille Guillerminet, Alban Roussel, Erwan Cossec-Kerloc'h, Stéphane Dufreneix

Abstract read
In one paragraph

Article in Radiation oncology (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
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  7. Review
  8. Deep Learning for CT Synthesis in Radiotherapy.Bioengineering (Basel, Switzerland) · 2025
    Review
  9. Article
  10. Article
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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.

Damien AutretInstitut de Cancérologie de l'Ouest, Angers, France.
Camille GuillerminetInstitut de Cancérologie de l'Ouest, Angers, France.
Alban RousselInstitut de Cancérologie de l'Ouest, Angers, France.
Erwan Cossec-Kerloc'hInstitut de Cancérologie de l'Ouest, Angers, France.
Stéphane DufreneixInstitut de Cancérologie de l'Ouest, Angers, France. stephane.dufreneix@ico.unicancer.fr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe interest in MR-only workflows is growing with the introduction of artificial intelligence in the synthetic CT generators converting MR images into CT images. The aim of this study was to evaluate several commercially available sCT generators for two anatomical localizations.

methodsFour sCT generators were evaluated: one based on the bulk density method and three based on deep learning methods. The comparison was performed on large patient cohorts (brain: 42 patients and pelvis: 52 patients). It included geometric accuracy with the evaluation of Hounsfield Units (HU) mean error (ME) for several structures like the body, bones and soft tissues. Dose evaluation included metrics like the D

resultsHU ME in the body were similar to those reported in the literature. D

conclusionsAll four generators can be used in clinical practice to implement a MR-only workflow but the bulk density method clearly performed worst in the brain.

Indexed as

Artificial IntelligenceProstateBrainHumansMalePelvisTomography, X-Ray ComputedWorkflowBulk density assignationDeep learning methodMR-only workflowSynthetic CT

Identifiers

PMID37670397
PMCPMC10478301

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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.