Evidence map›Paper›PMID 37771435›Full record

ArticleFrontiers in oncology2023

A real-time contouring feedback tool for consensus-based contour training.

Christopher L Nelson, Callistus Nguyen, Raymond Fang, Laurence E Court, Carlos E Cardenas, Dong Joo Rhee, Tucker J Netherton, Raymond P Mumme, Skylar Gay, Casey Gay and 8 more

Abstract read
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

18 authors.

Christopher L NelsonDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Callistus NguyenDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Raymond FangDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Laurence E CourtDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Carlos E CardenasDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Dong Joo RheeDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Tucker J NethertonDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Raymond P MummeDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Skylar GayDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Casey GayDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Barbara MarquezDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Mohammad D El BashaDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Yao ZhaoDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Mary GronbergDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Soleil HernandezDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Kelly A NealonDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Mary K MartelDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Jinzhong YangDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Kathrin Milbury · 1985 to 2026
$290.8M
NCI NIH HHS P30 CA016672
6 · The paper itself

Abstract

Purpose: Variability in contouring structures of interest for radiotherapy continues to be challenging. Although training can reduce such variability, having radiation oncologists provide feedback can be impractical. We developed a contour training tool to provide real-time feedback to trainees, thereby reducing variability in contouring. Methods: We developed a novel metric termed localized signed square distance (LSSD) to provide feedback to the trainee on how their contour compares with a reference contour, which is generated real-time by combining trainee contour and multiple expert radiation oncologist contours. Nine trainees performed contour training by using six randomly assigned training cases that included one test case of the heart and left ventricle (LV). The test case was repeated 30 days later to assess retention. The distribution of LSSD maps of the initial contour for the training cases was combined and compared with the distribution of LSSD maps of the final contours for all training cases. The difference in standard deviations from the initial to final LSSD maps, ΔLSSD, was computed both on a per-case basis and for the entire group. Results: For every training case, statistically significant ΔLSSD were observed for both the heart and LV. When all initial and final LSSD maps were aggregated for the training cases, before training, the mean LSSD ([range], standard deviation) was -0.8 mm ([-37.9, 34.9], 4.2) and 0.3 mm ([-25.1, 32.7], 4.8) for heart and LV, respectively. These were reduced to -0.1 mm ([-16.2, 7.3], 0.8) and 0.1 mm ([-6.6, 8.3], 0.7) for the final LSSD maps during the contour training sessions. For the retention case, the initial and final LSSD maps of the retention case were aggregated and were -1.5 mm ([-22.9, 19.9], 3.4) and -0.2 mm ([-4.5, 1.5], 0.7) for the heart and 1.8 mm ([-16.7, 34.5], 5.1) and 0.2 mm ([-3.9, 1.6],0.7) for the LV. Conclusions: A tool that uses real-time contouring feedback was developed and successfully used for contour training of nine trainees. In all cases, the utility was able to guide the trainee and ultimately reduce the variability of the trainee's contouring.

Indexed as

consensus contouringcontour trainingcontour variabilitylocalized signed surface distanceradiotherapy planning

Identifiers

PMID37771435
PMCPMC10525705

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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.