Evidence map›Paper›PMID 41144809›Full record

ArticleJournal of applied clinical medical physics2025

Intentional creation of suboptimal, realistic dose distributions.

Skylar S Gay, Mary P Gronberg, Raymond Mumme, Beth M Beadle, Anuja Jhingran, Tze Yee Lim, Zhiqian H Yu, Christine Chung, Meena Khan, Chelsea Pinnix and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Intentional creation of suboptimal, realistic dose distributions.Journal of applied clinical medical physics · 2025
    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

15 authors.

Skylar S GayDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.ORCID https://orcid.org/0000-0003-4659-0766
Mary P GronbergDepartment of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas, USA.ORCID https://orcid.org/0000-0002-0121-9579
Raymond MummeDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Beth M BeadleStanford University, Stanford, California, USA.ORCID https://orcid.org/0000-0001-5497-2831
Anuja JhingranDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Tze Yee LimDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Zhiqian H YuDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.ORCID https://orcid.org/0000-0002-2441-4457
Christine ChungDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.ORCID https://orcid.org/0000-0002-3093-6913
Meena KhanDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Chelsea PinnixDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Sanjay SheteDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Brent ParkerDepartment of Radiation Oncology, The University of Texas Medical Branch, Galveston, Texas, USA.ORCID https://orcid.org/0000-0001-7015-8805
Tucker J NethertonDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.ORCID https://orcid.org/0000-0003-1583-7121
Carlos E CardenasDepartment of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama, USA.
Laurence E CourtDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
NIH/NCI P30CA016672The Cancer Answers ScholarshipThe Dr. John J. Kopchick FellowshipThe Larry Deaven PhD Fellowship in Biomedical SciencesUniversity of Texas MD Anderson Cancer Center, Division of Radiation Oncology
6 · The paper itself

Abstract

backgroundRadiation oncology residents report a lack of understanding and confidence in assessing radiotherapy plan quality. A contributing factor is the environment in which plan review is taught during residency, that is, routine clinical practice, which does not provide ample time for self-guided practice in a low-stakes setting. Expertise in plan review requires diverse case presentation and many examples, which are often not achievable in smaller programs and for less common cancer types. As plan quality affects patient outcomes, it is important to address these pitfalls in the education of residents on plan review. PURPOSE: To address the identified pitfalls of clinic-based training, we have developed techniques to create realistic dose distributions that appear suboptimal in a controllable way. These plans can provide many more case examples in the training curriculum and present a low-stakes technique for safe and effective education of radiation oncology residents.

methodsHigh-quality dose distributions were first generated with a pre-trained deep learning model (trained using only high-quality plans). The dose distributions were then altered directly to create three classes of suboptimal dose distributions: (1) decreased organ-at-risk sparing, (2) decreased target conformality, and (3) hotspots in the target. Experienced clinicians then reviewed a subset of these suboptimal dose distributions to assess realism.

resultsWe successfully decreased the quality of radiotherapy dose distributions. The decreased organ-at-risk sparing, decreased target conformality, and increased target hotspots were statistically significant (p < 0.05) when assessed by dose-volume histogram metrics for all parameters evaluated, and the magnitude of dose change was controllable. The resulting dose distributions were overall scored by experienced clinicians as realistic.

conclusionIn this study, we developed techniques to generate realistic but suboptimal dose distributions. The techniques operate directly on existing dose distributions without the need for a treatment planning system and produce dose distributions that appear realistic to experienced clinicians.

Indexed as

Deep LearningInternship and ResidencyNeoplasmsOrgans at RiskRadiation OncologyRadiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-AssistedHumansRadiotherapy Dosageeducationplan quality

Identifiers

PMID41144809
PMCPMC12558600

What OpenQuestion holds

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LicenceCC BY
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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.