Evidence map›Paper›PMID 42080772›Full record

ArticleJournal of applied clinical medical physics2026

Development and clinical deployment of an automated planning tool for prostate only and male whole pelvis plans based on multi-criteria optimization.

Kai Huang, Kai Wang, Adam Schrum, Eric Kusmaul, Erica Fisler, Mariana Guerrero

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Article in Journal of applied clinical medical physics, 2026. 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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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Kai HuangDepartment of Radiation Oncology, University of Maryland Medical Center, Baltimore, Maryland, USA.
Kai WangDepartment of Radiation Oncology, University of Maryland Medical Center, Baltimore, Maryland, USA.
Adam SchrumDepartment Radiation Oncology, Central Maryland Radiation Oncology Center, Columbia, Maryland, USA.
Eric KusmaulDepartment Radiation Oncology, Baltimore Washington Medical Center Radiation Oncology, Glen Burnie, Maryland, USA.
Erica FislerDepartment Radiation Oncology, Kaufman Cancer Center, Upper Chesapeake Medical Center, Bel Air, Maryland, USA.
Mariana GuerreroDepartment of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA.ORCID https://orcid.org/0000-0003-4484-1349

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMulti-criteria optimization (MCO) is an advanced optimization technique that can be applied to any problem with multiple objectives that may be conflicting. MCO has been available in commercial treatment planning systems (TPS) for several years now and has been applied to treatment planning of many anatomical locations in a variety of ways. The MCO optimization method is based on the Pareto plans generation and is very powerful, but there are significant hurdles in terms of clinical implementation due to long computing times, lack of dosimetrists training and plan degradation after the optimized fluence is converted to deliverable. While some authors have studied the use of MCO in automation, no clinical implementation of an MCO-based auto-planning technique has been reported. PURPOSE: This study aims to develop and clinically deploy an automated planning tool based on MCO for prostate and whole-pelvis radiotherapy. MATERIALS AND

methodsA Python script based on a commercial treatment planning system was developed to automate MCO, including Pareto plan generation, fluence plan selection, dose conversion, and post-processing. The tool underwent retrospective validation on 10 prostate patients with the input of four dosimetrists and a 10-month prospective pilot involving another three senior dosimetrists across different community sites. Dosimetrists evaluated plan quality and provided quantitative and qualitative feedback for iterative improvements of the tool. The study reports on the plan comparisons between the clinical and the MCO generated plans for retrospective patients. The study also reports the prospective use cases and the qualitative and quantitative evaluations from dosimetrists.

resultsRetrospective evaluations showed 82.5% of MCO prostate plans were clinically acceptable. The tool generated prostate plans in approximately 10.1 min and whole pelvis plans in 27.2 min. Dosimetric analysis revealed comparable plan quality to clinical plans, with MCO plans achieving lower organ-at-risk doses. In the pilot phase, the MCO tool was used for 41 prospective patients, producing plans that dosimetrists could refine to achieve clinical acceptability within a median of 10 min.

conclusionsThis study demonstrates the successful development and clinical implementation of an MCO-based automated planning tool for generating acceptable VMAT plans for prostate and whole pelvis radiotherapy. The extensive pilot phase showcases an effective strategy for integrating automated planning solutions into routine clinical practice.

Indexed as

AlgorithmsPelvisProstatic NeoplasmsRadiotherapy Planning, Computer-AssistedAutomationHumansMaleOrgans at RiskPilot ProjectsProspective StudiesQuality Assurance, Health CareRadiotherapy DosageRadiotherapy, Intensity-ModulatedRetrospective Studiesauto‐planningmulti‐criteria optimizationprostate cancerradiotherapytreatment planning

Identifiers

PMID42080772
PMCPMC13137941

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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 Socratic graph.