Evidence map›Paper›PMID 39381612›Full record

ArticlePhysics and imaging in radiation oncology2024

Results of 2023 survey on the use of synthetic computed tomography for magnetic resonance Imaging-only radiotherapy: Current status and future steps.

M Fusella, E Alvarez Andres, F Villegas, L Milan, T M Janssen, R Dal Bello, C Garibaldi, L Placidi, D Cusumano

Abstract read
In one paragraph

Article in Physics and imaging in radiation oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. A universal medical imaging modality translation model in brain and head-and-neck radiotherapy.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026
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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

9 authors.

M FusellaAbano Terme Hospital, Department of Radiation Oncology, Abano Terme (Padua), Italy.
E Alvarez AndresOncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
F VillegasDepartment of Oncology-Pathology, Karolinska Institutet, Solna, Sweden.
L MilanMedical Physics Unit, Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale, Bellinzona, Switzerland.
T M JanssenDepartment of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, the Netherlands.
R Dal BelloUniversity Hospital Zurich and University of Zurich, Department of Radiation Oncology, Zurich, Switzerland.
C GaribaldiUnit of Radiation Research, IEO, European Institute of Oncology IRCCS, Milan, Italy.
L PlacidiFondazione Policlinico Universitario Agostino Gemelli, Medical Physics Unit, Roma, Italy.
D CusumanoMater Olbia Hospital, Department of Medical Physics, Olbia, (SS), Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and purpose: The emergence of synthetic CT (sCT) in MR-guided radiotherapy (MRgRT) represents a significant advancement, supporting MR-only workflows and online treatment adaptation. However, the lack of consensus guidelines has led to varied practices. This study reports results from a 2023 ESTRO survey aimed at defining current practices in sCT development and use. Materials and methods: An survey was distributed to ESTRO members, including 98 questions across four sections on sCT algorithm generation and usage. By June 2023, 100 centers participated. The survey revealed diverse clinical experiences and roles, with primary sCT use in the pelvis (60%), brain (15%), abdomen (11%), thorax (8%), and head-and-neck (6%). sCT was mostly used for conventional fractionation treatments (68%), photon SBRT (40%), and palliative cases (28%), with limited use in proton therapy (4%). Results: Conditional GANs and GANs were the most used neural network architectures, operating mainly on 1.5 T and 3 T MRI images. Less than half used paired images for training, and only 20% performed image selection. Key MR image quality parameters included magnetic field homogeneity and spatial integrity. Half of the respondents lacked a dedicated sCT-QA program, and many did not apply sanitychecks before calculation. Selection strategies included age, weight, and metal artifacts. A strong consensus (95%) emerged for vendor neutral guidelines. Conclusion: The survey highlights the need for expert-based, vendor-neutral guidelines to standardize sCT tools, metrics, and clinical protocols, ensuring effective sCT use in MR-guided radiotherapy.

Indexed as

Artificial intelligenceClinical implementationDeep learningMR-only planningMR-only radiotherapySurveySynthetic CT

Identifiers

PMID39381612
PMCPMC11460247

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.