Evidence map›Paper›PMID 36347220›Full record

ArticleJournal of applied clinical medical physics2023

Clinical application of a template-guided automated planning routine.

Matthew C Schmidt, Christopher D Abraham, Jiayi Huang, Clifford G Robinson, Geoffrey Hugo, Nels C Knutson, Baozhou Sun, Chipo Raranje, Erno Sajo, Piotr Zygmanski and 5 more

Full text read
In one paragraph

Article in Journal of applied clinical medical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Automated treatment planning for brain and spinal metastases.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2026
    Article
  3. Article
  4. Physics and imaging in radiation oncology · 2023
    Article
  5. Clinical application of a template-guided automated planning routine.Journal of applied clinical medical physics · 2023
    Article
  6. 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.

Matthew C SchmidtDepartment of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA.
Christopher D AbrahamDepartment of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA.
Jiayi HuangDepartment of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA.
Clifford G RobinsonDepartment of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA.
Geoffrey HugoDepartment of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA.
Nels C KnutsonDepartment of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA.
Baozhou SunDepartment of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA.
Chipo RaranjeDepartment of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA.
Erno SajoDepartment of Physics, University of Massachusetts Lowell, Lowell, Massachusetts, USA.
Piotr ZygmanskiBrigham and Women's/Dana Farber Cancer Institute/Harvard Medical School, Boston, Massachusetts, USA.
Marian JandelDepartment of Physics, University of Massachusetts Lowell, Lowell, Massachusetts, USA.
Peter SzentivanyiVarian Medical Systems, Palo Alto, California, USA.
Jessica HilliardDepartment of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA.
Jessica HamiltonDepartment of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA.
Francisco J ReynosoDepartment of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeDetermine the dosimetric quality and the planning time reduction when utilizing a template-based automated planning application.

methodsA software application integrated through the treatment planning system application programing interface, QuickPlan, was developed to facilitate automated planning using configurable templates for contouring, knowledge-based planning structure matching, field design, and algorithm settings. Validations are performed at various levels of the planning procedure and assist in the evaluation of readiness of the CT image, structure set, and plan layout for automated planning. QuickPlan is evaluated dosimetrically against 22 hippocampal-avoidance whole brain radiotherapy patients. The required times to treatment plan generation are compared for the validations set as well as 10 prospective patients whose plans have been automated by QuickPlan.

resultsThe generations of 22 automated treatment plans are compared against a manual replanning using an identical process, resulting in dosimetric differences of minor clinical significance. The target dose to 2% volume and homogeneity index result in significantly decreased values for automated plans, whereas other dose metric evaluations are nonsignificant. The time to generate the treatment plans is reduced for all automated plans with a median difference of 9' 50″ ± 4' 33″.

conclusionsTemplate-based automated planning allows for reduced treatment planning time with consistent optimization structure creation, treatment field creation, plan optimization, and dose calculation with similar dosimetric quality. This process has potential expansion to numerous disease sites.

Indexed as

Radiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-AssistedHumansProspective StudiesRadiotherapy DosageSoftwareautomated planningESAPIknowledge-based planningtreatment planning

Identifiers

PMID36347220
PMCPMC10018666

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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.