Evidence map›Paper›PMID 29218841›Full record

ArticleJournal of applied clinical medical physics2018

Improvement of conformal arc plans by using deformable margin delineation method for stereotactic lung radiotherapy.

Görkem Güngör, Melek Demir, Gökhan Aydın, Bülent Yapıcı, Banu Atalar, Enis Özyar

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Article in Journal of applied clinical medical physics, 2018. 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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5 · Who and what money

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6 authors.

Görkem GüngörDepartment of Radiation Oncology, Acıbadem University School of Medicine, Istanbul, Turkey.
Melek DemirDepartment of Radiation Oncology, Acıbadem University School of Medicine, Istanbul, Turkey.
Gökhan AydınDepartment of Radiation Oncology, Acıbadem University School of Medicine, Istanbul, Turkey.
Bülent YapıcıDepartment of Radiation Oncology, Acıbadem University School of Medicine, Istanbul, Turkey.
Banu AtalarDepartment of Radiation Oncology, Acıbadem University School of Medicine, Istanbul, Turkey.
Enis ÖzyarDepartment of Radiation Oncology, Acıbadem University School of Medicine, Istanbul, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeStereotactic body radiotherapy (SBRT) is an established treatment technique in the management of medically inoperable early stage non-small cell lung cancer (NSCLC). Different techniques such as volumetric modulated arc (VMAT) and three-dimensional conformal arc (DCA) can be used in SBRT. Previously, it has been shown that VMAT is superior to DCA technique in terms of plan evaluation parameters. However, DCA technique has several advantages such as ease of use and considerable shortening of the treatment time. DCA technique usually results in worse conformity which is not possible to ameliorate by inverse optimization. In this study, we aimed to analyze whether a simple method - deformable margin delineation (DMD) - improves the quality of the DCA technique, reaching similar results to VMAT in terms of plan evaluation parameters.

methodsTwenty stage I-II (T1-2, N0, M0) NSCLC patients were included in this retrospective dosimetric study. Noncoplanar VMAT and conventional DCA plans were generated using 6 MV and 10 MV with flattening filter free (FFF) photon energies. The DCA plan with 6FFF was calculated and 95% of the PTV was covered by the prescription isodose line. Hot dose regions (receiving dose over 100% of prescription dose) outside PTV and cold dose regions (receiving dose under 100% of prescription dose) inside PTV were identified. A new PTV (PTV-DMD) was delineated by deforming PTV margin with respect to hot and cold spot regions obtained from conventional DCA plans. Dynamic multileaf collimators (MLC) were set to PTV-DMD beam eye view (BEV) positions and the new DCA plans (DCA-DMD) with 6FFF were generated. Three-dimensional (3D) dose calculations were computed for PTV-DMD volume. However, the prescription isodose was specified and normalized to cover 95% volume of original PTV. Several conformity indices and lung doses were compared for different treatment techniques.

resultsDCA-DMD method significantly achieved a superior conformity index (CI), conformity number (CI

conclusionsOur results demonstrated that DCA plan evaluation parameters can be ameliorated by using the DMD method. This new method improves DCA plan quality and reaches similar results with VMAT in terms of dosimetric parameters. We believe that DCA-DMD is a simple and effective technique for SBRT and can be preferred due to shorter treatment and planning time.

Indexed as

Carcinoma, Non-Small-Cell LungHumansLung NeoplasmsOrgans at RiskRadiosurgeryRadiotherapy, ConformalRadiotherapy DosageRadiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-AssistedRetrospective StudiesDCAFFFSBRTVMAT

Identifiers

PMID29218841
PMCPMC5768002

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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.