Evidence map›Paper›PMID 38549943›Full record

ArticleFrontiers in oncology2024

Clinical acceptance and dosimetric impact of automatically delineated elective target and organs at risk for head and neck MR-Linac patients.

Vesela Koteva, Björn Eiben, Alex Dunlop, Amit Gupta, Tarun Gangil, Kee Howe Wong, Sebastiaan Breedveld, Simeon Nill, Kevin Harrington, Uwe Oelfke

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.2field-weighted citation impact, top 25% of its field
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

3 citing papers in PubMed, 2 citations in OpenAlex.

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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

10 authors at 4 institutions in 2 countries.

Vesela KotevaRadiotherapy Physics Modelling, Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom.
Björn EibenRadiotherapy Physics Modelling, Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom.
Alex DunlopThe Joint Department of Physics, The Royal Marsden Hospital and The Institute of Cancer Research, London, United Kingdom.
Amit GuptaHead and Neck Unit, The Royal Marsden National Health Service (NHS) Foundation Trust and The Institute of Cancer Research, London, United Kingdom.
Tarun GangilRadiotherapy Physics Modelling, Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom.
Kee Howe WongHead and Neck Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom.
Sebastiaan BreedveldDepartment of Radiotherapy, Erasmus University Medical Center (MC) Rotterdam, Rotterdam, Netherlands.
Simeon NillRadiotherapy Physics Modelling, Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom.
Kevin HarringtonTargeted Radiotherapy, Department of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom.
Uwe OelfkeRadiotherapy Physics Modelling, Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom.
Institute of Cancer Research · GBNational Health Service · GBRoyal Marsden Hospital · GBErasmus University Rotterdam · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: MR-Linac allows for daily online treatment adaptation to the observed geometry of tumor targets and organs at risk (OARs). Manual delineation for head and neck cancer (HNC) patients takes 45-75 minutes, making it unsuitable for online adaptive radiotherapy. This study aims to clinically and dosimetrically validate an in-house developed algorithm which automatically delineates the elective target volume and OARs for HNC patients in under a minute. Methods: Auto-contours were generated by an in-house model with 2D U-Net architecture trained and tested on 52 MRI scans via leave-one-out cross-validation. A randomized selection of 684 automated and manual contours (split half-and-half) was presented to an oncologist to perform a blind test and determine the clinical acceptability. The dosimetric impact was investigated for 13 patients evaluating the differences in dosage for all structures. Results: Automated contours were generated in 8 seconds per MRI scan. The blind test concluded that 114 (33%) of auto-contours required adjustments with 85 only minor and 15 (4.4%) of manual contours required adjustments with 12 only minor. Dosimetric analysis showed negligible dosimetric differences between clinically acceptable structures and structures requiring minor changes. The Dice Similarity coefficients for the auto-contours ranged from 0.66 ± 0.11 to 0.88 ± 0.06 across all structures. Conclusion: Majority of auto-contours were clinically acceptable and could be used without any adjustments. Majority of structures requiring minor adjustments did not lead to significant dosimetric differences, hence manual adjustments were needed only for structures requiring major changes, which takes no longer than 10 minutes per patient.

Indexed as

automated delineationclinical acceptabilitydosimetric impacthead and neck cancerMR-Linac

Identifiers

PMID38549943
PMCPMC10976943
OpenAlexW4392805085

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.