Evidence map›Paper›PMID 40841188›Full record

ArticleJournal of applied clinical medical physics2025

Optimizing lung SBRT delivery: A hybrid approach combining dynamic conformal arc (DCA) and volumetric modulated arc therapy (VMAT) techniques.

Karen Chin Snyder, Amanda DiCarlo, Jessie Huang, Bo Zhao, Yimei Huang, Kundan Thind

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Article in Journal of applied clinical medical physics, 2025. 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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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Karen Chin SnyderDepartment of Radiation Oncology, Henry Ford Health System, Detroit, Michigan, USA.ORCID https://orcid.org/0000-0002-9241-7430
Amanda DiCarloDepartment of Radiation Oncology, Henry Ford Health System, Detroit, Michigan, USA.
Jessie HuangDepartment of Radiation Oncology, Henry Ford Health System, Detroit, Michigan, USA.
Bo ZhaoDepartment of Radiation Oncology, Henry Ford Health System, Detroit, Michigan, USA.
Yimei HuangDepartment of Radiation Oncology, Henry Ford Health System, Detroit, Michigan, USA.ORCID https://orcid.org/0000-0002-1589-4276
Kundan ThindDepartment of Radiation Oncology, Henry Ford Health System, Detroit, Michigan, USA.ORCID https://orcid.org/0000-0002-8261-6655

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study introduces and evaluates a hybrid dynamic conformal arc-volumetric modulated arc therapy (hDCA-VMAT) technique for lung stereotactic body radiotherapy (SBRT). The goal is to combine the planning efficiency of VMAT with the delivery robustness of dynamic conformal arc (DCA) techniques, particularly for low-density lung targets where motion and dose calculation uncertainties pose challenges.

methodsTwenty-four previously treated lung SBRT cases were retrospectively replanned using hDCA-VMAT, conventional VMAT, and aperture controlled VMAT (VMAT_AC). hDCA-VMAT plans were initiated with a manually created DCA plan, followed by limited inverse optimization with constrained aperture modulation. Plans were created in Eclipse v16.1 and calculated using the AcurosXB algorithm. Dosimetric plan quality, beam complexity, and delivery efficiency were assessed. Complexity was quantified using aperture-based metrics (e.g., average leaf pair opening, beam area, modulation index). Pretreatment delivery accuracy was evaluated via EPID-based gamma analysis at 3%/1, 2%/1, and 1%/1 mm Gamma criteria.

resultsAll techniques produced clinically acceptable plans. Target coverage and conformity indices were comparable, but hDCA-VMAT plans demonstrated reduced mid-dose spread and significantly lower modulation. hDCA-VMAT achieved the lowest modulation factor (2.1 ± 0.52) and shortest beam on time (1.74 ± 0.46 min), a 27%-30% reduction compared to VMAT and VMAT_AC. Beam complexity metrics confirmed larger, more circular apertures for hDCA-VMAT. Gamma pass rates were significantly higher for hDCA-VMAT across all criteria, particularly under stringent 1%/1 mm criteria.

conclusionThe hDCA-VMAT technique offers a practical, streamlined approach for lung SBRT planning that reduces modulation while maintaining high plan quality. By initiating with a DCA plan and applying limited optimization only when necessary, hDCA-VMAT minimizes planning complexity and improves delivery efficiency. These benefits are especially relevant for treating low-density lung tumors, where robustness to motion and delivery accuracy are critical.

Indexed as

AlgorithmsLung NeoplasmsRadiosurgeryRadiotherapy, ConformalRadiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-AssistedHumansOrgans at RiskRadiotherapy DosageRetrospective Studiesdeliverylimited optimizationlung SBRTmotion management

Identifiers

PMID40841188
PMCPMC12795709

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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.