Evidence map›Paper›PMID 38642406›Full record

ArticleJournal of applied clinical medical physics2024

Automated treatment planning for whole breast irradiation with individualized tangential IMRT fields.

Giulianne Rivelli Rodrigues Zaratim, Ricardo Gomes Dos Reis, Marcos Antônio Dos Santos, Nathalya Ala Yagi, Luis Felipe Oliveira E Silva

Abstract read
In one paragraph

Article in Journal of applied clinical medical physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. 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
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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

5 authors.

Giulianne Rivelli Rodrigues ZaratimDepartment of Radiation Oncology, CONFIAR Radiotherapy, Goiânia, Goiás, Brazil.ORCID https://orcid.org/0009-0007-4217-0437
Ricardo Gomes Dos ReisDepartment of Radiation Oncology, University Hospital of Brasilia, Brasilia, Federal District, Brazil.ORCID https://orcid.org/0000-0003-4266-7690
Marcos Antônio Dos SantosDepartment of Radiation Oncology, CONFIAR Radiotherapy, Goiânia, Goiás, Brazil.ORCID https://orcid.org/0000-0002-7055-4397
Nathalya Ala YagiDepartment of Radiation Oncology, CONFIAR Radiotherapy, Goiânia, Goiás, Brazil.ORCID https://orcid.org/0009-0001-1652-0671
Luis Felipe Oliveira E SilvaDepartment of Radiation Oncology, CONFIAR Radiotherapy, Goiânia, Goiás, Brazil.ORCID https://orcid.org/0009-0002-6453-9403

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposesThis study aimed to develop and validate algorithms for automating intensity modulated radiation therapy (IMRT) planning in breast cancer patients, with a focus on patient anatomical characteristics. MATERIAL AND

methodsWe retrospectively selected 400 breast cancer patients without lymph node involvement for automated treatment planning. Automation was achieved using the Eclipse Scripting Application Programming Interface (ESAPI) integrated into the Eclipse Treatment Planning System. We employed three beam insertion geometries and three optimization strategies, resulting in 3600 plans, each delivering a 40.05 Gy dose in 15 fractions. Gantry angles in the tangent fields were selected based on a criterion involving the minimum intersection area between the Planning Target Volume (PTV) and the ipsilateral lung in the Beam's Eye View projection. ESAPI was also used to gather patient anatomical data, serving as input for Random Forest models to select the optimal plan. The Random Forest classification considered both beam insertion geometry and optimization strategy. Dosimetric data were evaluated in accordance with the Radiation Therapy Oncology Group (RTOG) 1005 protocol.

resultsOverall, all approaches generated high-quality plans, with approximately 94% meeting the acceptable dose criteria for organs at risk and/or target coverage as defined by RTOG guidelines. Average automated plan generation time ranged from 6 min and 37 s to 9 min and 22 s, with the mean time increasing with additional fields. The Random Forest approach did not successfully enable automatic planning strategy selection. Instead, our automated planning system allows users to choose from the tested geometry and strategy options.

conclusionsAlthough our attempt to correlate patient anatomical features with planning strategy using machine learning tools was unsuccessful, the resulting dosimetric outcomes proved satisfactory. Our algorithm consistently produced high-quality plans, offering significant time and efficiency advantages.

Indexed as

AlgorithmsBreast NeoplasmsOrgans at RiskRadiotherapy DosageRadiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-AssistedAutomationFemaleHumansPrognosisRetrospective Studiesartificial intelligenceautomated planningbreast cancerradiation therapy

Identifiers

PMID38642406
PMCPMC11087165

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Registered trials

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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.