Dosimetric evaluation of LINAC-based single-isocenter multi-target multi-fraction stereotactic radiosurgery with more than 20 targets: comparing MME, HyperArc, and RapidArc Article Swipe
Hyunuk Jung
,
Jihyung Yoon
,
Olga Dona Lemus
,
Sean Tanny
,
Yuwei Zhou
,
Michael T. Milano
,
Kenneth Y. Usuki
,
Sara Hardy
,
Dandan Zheng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s13014-024-02416-7
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s13014-024-02416-7
Background To compare the dosimetric quality of three widely used techniques for LINAC-based single-isocenter multi-target multi-fraction stereotactic radiosurgery (fSRS) with more than 20 targets: dynamic conformal arc (DCA) in BrainLAB Multiple Metastases Elements (MME) module and volumetric modulated arc therapy (VMAT) using RapidArc (RA) and HyperArc (HA) in Varian Eclipse. Methods Ten patients who received single-isocenter fSRS with 20–37 targets were retrospectively replanned using MME, RA, and HA. Various dosimetric parameters, such as conformity index (CI), Paddick CI, gradient index (GI), normal brain dose exposures, maximum organ-at-risk (OAR) doses, and beam-on times were extracted and compared among the three techniques. Wilcoxon signed-rank test was used for statistical analysis. Results All plans achieved the prescribed dose coverage goal of at least 95% of the planning target volume (PTV). HA plans showed superior conformity compared to RA and MME plans. MME plans showed superior GI compared to RA and HA plans. RA plans resulted in significantly higher low and intermediate dose exposure to normal brain compared to HA and MME plans, especially for lower doses of ≥ 8Gy and ≥ 5Gy. No significant differences were observed in the maximum dose to OARs among the three techniques. The beam-on time of MME plans was about two times longer than RA and HA plans. Conclusions HA plans achieved the best conformity, while MME plans achieved the best dose fall-off for LINAC-based single-isocenter multi-target multi-fraction SRS with more than 20 targets. The choice of the optimal technique should consider the trade-offs between dosimetric quality, beam-on time, and planning effort.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s13014-024-02416-7
- https://ro-journal.biomedcentral.com/counter/pdf/10.1186/s13014-024-02416-7
- OA Status
- gold
- Cited By
- 12
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391610902
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Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4391610902Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1186/s13014-024-02416-7Digital Object Identifier
- Title
-
Dosimetric evaluation of LINAC-based single-isocenter multi-target multi-fraction stereotactic radiosurgery with more than 20 targets: comparing MME, HyperArc, and RapidArcWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-07Full publication date if available
- Authors
-
Hyunuk Jung, Jihyung Yoon, Olga Dona Lemus, Sean Tanny, Yuwei Zhou, Michael T. Milano, Kenneth Y. Usuki, Sara Hardy, Dandan ZhengList of authors in order
- Landing page
-
https://doi.org/10.1186/s13014-024-02416-7Publisher landing page
- PDF URL
-
https://ro-journal.biomedcentral.com/counter/pdf/10.1186/s13014-024-02416-7Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ro-journal.biomedcentral.com/counter/pdf/10.1186/s13014-024-02416-7Direct OA link when available
- Concepts
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Isocenter, Medicine, Radiosurgery, Nuclear medicine, Linear particle accelerator, Radiation treatment planning, Truebeam, Radiation therapy, Medical physics, Beam (structure), Radiology, Optics, Imaging phantom, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
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2025: 9, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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26Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.replanned | 63 |
| abstract_inverted_index.technique | 242 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Metastases | 32 |
| abstract_inverted_index.conformity | 74, 132 |
| abstract_inverted_index.dosimetric | 5, 70, 248 |
| abstract_inverted_index.especially | 170 |
| abstract_inverted_index.exposures, | 85 |
| abstract_inverted_index.prescribed | 114 |
| abstract_inverted_index.techniques | 11 |
| abstract_inverted_index.trade-offs | 246 |
| abstract_inverted_index.volumetric | 37 |
| abstract_inverted_index.Conclusions | 211 |
| abstract_inverted_index.LINAC-based | 13, 227 |
| abstract_inverted_index.conformity, | 217 |
| abstract_inverted_index.differences | 182 |
| abstract_inverted_index.parameters, | 71 |
| abstract_inverted_index.signed-rank | 102 |
| abstract_inverted_index.significant | 181 |
| abstract_inverted_index.statistical | 107 |
| abstract_inverted_index.techniques. | 100, 194 |
| abstract_inverted_index.intermediate | 158 |
| abstract_inverted_index.multi-target | 15, 229 |
| abstract_inverted_index.radiosurgery | 18 |
| abstract_inverted_index.stereotactic | 17 |
| abstract_inverted_index.organ-at-risk | 87 |
| abstract_inverted_index.significantly | 154 |
| abstract_inverted_index.multi-fraction | 16, 230 |
| abstract_inverted_index.retrospectively | 62 |
| abstract_inverted_index.single-isocenter | 14, 56, 228 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5103489638 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I5388228 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.46000000834465027 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile.value | 0.98815224 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |