Evidence map›Paper›PMID 34432946›Full record

ArticleJournal of applied clinical medical physics2021

A comparison of two methodologies for radiotherapy treatment plan optimization and QA for clinical trials.

Huaizhi Geng, Tawfik Giaddui, Chingyun Cheng, Haoyu Zhong, Samuel Ryu, Zhongxing Liao, Fang-Fang Yin, Michael Gillin, Radhe Mohan, Ying Xiao

Open access · goldAbstract read
In one paragraph

Article in Journal of applied clinical medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.5field-weighted citation impact, top 11% 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

10 citing papers in PubMed, 12 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 1 country.

Huaizhi GengDepartment of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0000-0002-9804-3492
Tawfik GiadduiDepartment of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Chingyun ChengDepartment of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Haoyu ZhongDepartment of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Samuel RyuStony Brook University Medical Center, Stony Brook, New York, USA.
Zhongxing LiaoMD Anderson Cancer Center, Houston, Texas, USA.
Fang-Fang YinDuke University Medical Center, Durham, North Carolina, USA.
Michael GillinMD Anderson Cancer Center, Houston, Texas, USA.
Radhe MohanMD Anderson Cancer Center, Houston, Texas, USA.
Ying XiaoDepartment of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
University of Pennsylvania · USThe University of Texas MD Anderson Cancer Center · USDuke Medical Center · USStony Brook University Hospital · US

Funding

NRG Oncology Network Group Operations Center - GY9 BIQSFP Reports/BudgetsU10CA180868 · NCI · NRG ONCOLOGY FOUNDATION, INC. · PI NORMAN WOLMARK · 2014 to 2026
$206.8M
Statistics CoreU10CA180822 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Mei-Yin C Polley · 2014 to 2026
$146.4M
IROC: Enhancing local enrolling site radiological data capture capabilities for NCTN trialsU24CA180803 · NCI · AMERICAN COLLEGE OF RADIOLOGY · PI Thomas J. FitzGerald, MICHAEL V KNOPP · 2014 to 2026
$116.2M
IROC Philadelphia RT QA; This project was supported by grants U10CA180868 (NRG Oncology Operations), U10CA180822 (NRG Oncology SDMC), and U24CA180803 (IROC), from the National Cancer Institute, and in part by a grant from the Pennsylvania Department of Health. The Department specifically disclaims responsibility for any analyses, interpretations, or conclusions by Eli LillyNCI NIH HHS U10 CA180822NCI NIH HHS U10 CA180868NCI NIH HHS U24 CA180803
6 · The paper itself

Abstract

background and purposeThe efficacy of clinical trials and the outcome of patient treatment are dependent on the quality assurance (QA) of radiation therapy (RT) plans. There are two widely utilized approaches that include plan optimization guidance created based on patient-specific anatomy. This study examined these two techniques for dose-volume histogram predictions, RT plan optimizations, and prospective QA processes, namely the knowledge-based planning (KBP) technique and another first principle (FP) technique.

methodsThis analysis included 60, 44, and 10 RT plans from three Radiation Therapy Oncology Group (RTOG) multi-institutional trials: RTOG 0631 (Spine SRS), RTOG 1308 (NSCLC), and RTOG 0522 (H&N), respectively. Both approaches were compared in terms of dose prediction and plan optimization. The dose predictions were also compared to the original plan submitted to the trials for the QA procedure.

resultsFor the RTOG 0631 (Spine SRS) and RTOG 0522 (H&N) plans, the dose predictions from both techniques have correlation coefficients of >0.9. The RT plans that were re-optimized based on the predictions from both techniques showed similar quality, with no statistically significant differences in target coverage or organ-at-risk sparing. The predictions of mean lung and heart doses from both methods for RTOG1308 patients, on the other hand, have a discrepancy of up to 14 Gy.

conclusionsBoth methods are valuable tools for optimization guidance of RT plans for Spine SRS and Head and Neck cases, as well as for QA purposes. On the other hand, the findings suggest that KBP may be more feasible in the case of inoperable lung cancer patients who are treated with IMRT plans that have spatially unevenly distributed beam angles.

Indexed as

Lung NeoplasmsRadiotherapy, Intensity-ModulatedHumansOrgans at RiskProspective StudiesRadiotherapy DosageRadiotherapy Planning, Computer-Assistedknowledge based planningPlanIQradiotherapy quality assuranceRapidPlan

Identifiers

PMID34432946
PMCPMC8504592
OpenAlexW3194176819

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.