Evidence map›Paper›PMID 38633281›Full record

ArticlePhysics and imaging in radiation oncology2024

Large-scale dose evaluation of deep learning organ contours in head-and-neck radiotherapy by leveraging existing plans.

Prerak Mody, Merle Huiskes, Nicolas F Chaves-de-Plaza, Alice Onderwater, Rense Lamsma, Klaus Hildebrandt, Nienke Hoekstra, Eleftheria Astreinidou, Marius Staring, Frank Dankers

Open access · goldAbstract 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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. 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

10 authors at 2 institutions in 1 country.

Prerak ModyDivision of Image Processing (LKEB), Department of Radiology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands.
Merle HuiskesDepartment of Radiation Oncology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands.
Nicolas F Chaves-de-PlazaHollandPTC consortium - Erasmus Medical Center, Rotterdam, Holland Proton Therapy Centre, Delft, Leiden University Medical Center (LUMC), Leiden and Delft University of Technology, Delft, The Netherlands.
Alice OnderwaterDepartment of Radiation Oncology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands.
Rense LamsmaDepartment of Radiation Oncology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands.
Klaus HildebrandtComputer Graphics and Visualization Group, EEMCS, TU Delft, Delft 2628 CD, The Netherlands.
Nienke HoekstraDepartment of Radiation Oncology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands.
Eleftheria AstreinidouDepartment of Radiation Oncology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands.
Marius StaringDivision of Image Processing (LKEB), Department of Radiology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands.
Frank DankersDepartment of Radiation Oncology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands.
Leiden University Medical Center · NLDelft University of Technology · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and purpose: Retrospective dose evaluation for organ-at-risk auto-contours has previously used small cohorts due to additional manual effort required for treatment planning on auto-contours. We aimed to do this at large scale, by a) proposing and assessing an automated plan optimization workflow that used existing clinical plan parameters and b) using it for head-and-neck auto-contour dose evaluation. Materials and methods: Our automated workflow emulated our clinic's treatment planning protocol and reused existing clinical plan optimization parameters. This workflow recreated the original clinical plan ( Results: For plan recreation ( Conclusions: The plan replication capability of our automated program provides a blueprint for other clinics to perform auto-contour dose evaluation with large patient cohorts. Finally, despite geometric differences, auto-contours had a minimal median dose impact, hence inspiring confidence in their utility and facilitating their clinical adoption.

Indexed as

Auto contouringAutomated plan optimizationAutomated plansDose impactRobot process automation

Identifiers

PMID38633281
PMCPMC11021837
OpenAlexW4393282787

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.