Evidence map›Paper›PMID 40248770›Full record

ArticlePhysics and imaging in radiation oncology2025

Multicentre prospective risk analysis of a fully automated radiotherapy workflow.

Geert De Kerf, Ana Barragán-Montero, Charlotte L Brouwer, Pietro Pisciotta, Marie-Claude Biston, Marco Fusella, Geoffroy Herbin, Esther Kneepkens, Livia Marrazzo, Joshua Mason and 5 more

Abstract read
In one paragraph

Article in Physics and imaging in radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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  4. Review
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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

15 authors.

Geert De KerfDepartment of Radiation Oncology, Iridium Netwerk, Wilrijk (Antwerp), Belgium.
Ana Barragán-MonteroUCLouvain - Institut de Recherche Expérimentale et Clinique - Molecular Imaging Radiotherapy and Oncology (MIRO), Brussels, Belgium.
Charlotte L BrouwerUniversity of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, the Netherlands.
Pietro PisciottaUniversity of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, the Netherlands.
Marie-Claude BistonCentre Léon Bérard, 28 rue Laennec 69373 LYON Cedex 08, France.
Marco FusellaAbano Terme Hospital, Department of Radiation Oncology, Abano Terme (Padua), Italy.
Geoffroy HerbinIon Beam Applications, Louvain-la-Neuve, Belgium.
Esther KneepkensDepartment of Radiation Oncology (Maastro, GROW School for Oncology, Maastricht University Medical Center+, Maastricht, the Netherlands.
Livia MarrazzoUniversity of Florence, Department of Experimental and Clinical Biomedical Sciences, Florence, Italy.
Joshua MasonImperial College Healthcare NHS Trust, London, UK.
Camila Panduro NielsenLaboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense, Denmark.
Koen SnijdersDepartment of Radiation Oncology, Iridium Netwerk, Wilrijk (Antwerp), Belgium.
Stephanie Tanadini-LangDepartment of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
Aude VaanderingRadiation Therapy Department, Cliniques Universitaires St Luc, Brussels, Belgium.
Tomas M JanssenDepartment of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Purpose: Fully automated workflows (FAWs) for radiotherapy treatment preparation are feasible, but remain underutilized in clinical settings. A multicentre prospective risk analysis was conducted to support centres in managing FAW-related risks and to identify workflow steps needing improvement. Material and Methods: Eight European radiotherapy centres performed a failure mode and effect analysis (FMEA) on a hypothetical FAW, with a manual review step at the end. Centres assessed occurrence, severity and detectability of provided, or newly added, failure modes to obtain a risk score. Quantitative analysis was performed on curated data, while qualitative analysis summarized free text comments. Results: Manual review and auto-segmentation were identified as the highest-risk steps and the highest scoring failure modes were associated with inadequate manual review (high detectability and severity score), incorrect (i.e. outside of intended use) application of the FAW (high severity score) and protocol violations during patient preparation (high occurrence score). The qualitative analysis highlighted amongst others the risk of deviation from protocol and the difficulty for manual review to recognize automation errors. The risk associated with the technical parts of the workflow was considered low. Conclusions: The FMEA analysis highlighted that points where people interact with the FAW were considered higher risk than lack of trust in the FAW itself. Major concerns were the ability of people to correctly judge output in case of low generalizability and increasing skill degradation. Consequently, educational programs and interpretative tools are essential prerequisites for widespread clinical application of FAWs.

Indexed as

Auto-contouringAutomationAuto-planningComputer assisted radiotherapy planningFMEA

Identifiers

PMID40248770
PMCPMC12005333

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.