Multi‐institution single geometry plan complexity characteristics based on IROC phantoms.
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| Title: | Multi‐institution single geometry plan complexity characteristics based on IROC phantoms. |
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| Authors: | Desai, Vimal1 (AUTHOR) vimal.desai@jefferson.edu, Labby, Zacariah2 (AUTHOR), Culberson, Wesley3 (AUTHOR), DeWerd, Larry3 (AUTHOR), Kry, Stephen4 (AUTHOR) |
| Source: | Medical Physics. Aug2024, Vol. 51 Issue 8, p5693-5707. 15p. |
| Subjects: | Principal components analysis, Databases, Planning techniques, Radiotherapy, Quality assurance, Lungs |
| Abstract: | Background: Clinical intensity modulated radiation therapy plans have been described using various complexity metrics to help identify problematic radiotherapy plans. Most previous studies related to the quantification of plan complexity and their utility have relied on institution‐specific plans which can be highly variable depending on the machines, planning techniques, delivery modalities, and measurement devices used. In this work, 1723 plans treating one of only four standardized geometries were simultaneously analyzed to investigate how radiation plan complexity metrics vary across four different sets of common objectives. Purpose: To assess the treatment plan complexity characteristics of plans developed for Imaging and Radiation Oncology Core (IROC) phantoms. Specifically, to understand the variability in plan complexity between institutions for a common plan objective, and to evaluate how various complexity metrics differentiate relevant groups of plans. Methods: 1723 plans treating one of four standardized IROC phantom geometries representing four different anatomical sites of treatment were analyzed. For each plan, 22 MLC‐descriptive plan complexity metrics were calculated, and principal component analysis (PCA) was applied to the 22 metrics in order to evaluate differences in plan complexity between groups. Across all metrics, pairwise comparisons of the IROC phantom data were made for the following classifications of the data: anatomical phantom treated, treatment planning system (TPS), and the combination of MLC model and treatment planning system. An objective k‐means clustering algorithm was also applied to the data to determine if any meaningful distinctions could be made between different subgroups. The IROC phantom database was also compared to a clinical database from the University of Wisconsin‐Madison (UW) which included plans treating the same four anatomical sites as the IROC phantoms using a TrueBeam™ STx and Pinnacle3 TPS. Results: The IROC head and neck and spine plans were distinct from the prostate and lung plans based on comparison of the 22 metrics. All IROC phantom plan group complexity metric distributions were highly variable despite all plans being designed for identical geometries and plan objectives. The clusters determined by the k‐means algorithm further supported that the IROC head and neck and spine plans involved similar amounts of complexity and were largely distinct from the prostate and lung plans, but no further distinctions could be made. Plan complexity in the head and neck and spine IROC phantom plans were similar to the complexity encountered in the UW clinical plans. Conclusions: There is substantial variability in plan complexity between institutions when planning for the same objective. For each IROC anatomical phantom treated, the magnitude of variability in plan complexity between institutions is similar to the variability in plan complexity encountered within a single institution database containing several hundred unique clinical plans treating corresponding anatomies in actual patients. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 179046684 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multi‐institution single geometry plan complexity characteristics based on IROC phantoms. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Desai%2C+Vimal%22">Desai, Vimal</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> vimal.desai@jefferson.edu</i><br /><searchLink fieldCode="AR" term="%22Labby%2C+Zacariah%22">Labby, Zacariah</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Culberson%2C+Wesley%22">Culberson, Wesley</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22DeWerd%2C+Larry%22">DeWerd, Larry</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kry%2C+Stephen%22">Kry, Stephen</searchLink><relatesTo>4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Aug2024, Vol. 51 Issue 8, p5693-5707. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink><br /><searchLink fieldCode="DE" term="%22Planning+techniques%22">Planning techniques</searchLink><br /><searchLink fieldCode="DE" term="%22Radiotherapy%22">Radiotherapy</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+assurance%22">Quality assurance</searchLink><br /><searchLink fieldCode="DE" term="%22Lungs%22">Lungs</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Clinical intensity modulated radiation therapy plans have been described using various complexity metrics to help identify problematic radiotherapy plans. Most previous studies related to the quantification of plan complexity and their utility have relied on institution‐specific plans which can be highly variable depending on the machines, planning techniques, delivery modalities, and measurement devices used. In this work, 1723 plans treating one of only four standardized geometries were simultaneously analyzed to investigate how radiation plan complexity metrics vary across four different sets of common objectives. Purpose: To assess the treatment plan complexity characteristics of plans developed for Imaging and Radiation Oncology Core (IROC) phantoms. Specifically, to understand the variability in plan complexity between institutions for a common plan objective, and to evaluate how various complexity metrics differentiate relevant groups of plans. Methods: 1723 plans treating one of four standardized IROC phantom geometries representing four different anatomical sites of treatment were analyzed. For each plan, 22 MLC‐descriptive plan complexity metrics were calculated, and principal component analysis (PCA) was applied to the 22 metrics in order to evaluate differences in plan complexity between groups. Across all metrics, pairwise comparisons of the IROC phantom data were made for the following classifications of the data: anatomical phantom treated, treatment planning system (TPS), and the combination of MLC model and treatment planning system. An objective k‐means clustering algorithm was also applied to the data to determine if any meaningful distinctions could be made between different subgroups. The IROC phantom database was also compared to a clinical database from the University of Wisconsin‐Madison (UW) which included plans treating the same four anatomical sites as the IROC phantoms using a TrueBeam™ STx and Pinnacle3 TPS. Results: The IROC head and neck and spine plans were distinct from the prostate and lung plans based on comparison of the 22 metrics. All IROC phantom plan group complexity metric distributions were highly variable despite all plans being designed for identical geometries and plan objectives. The clusters determined by the k‐means algorithm further supported that the IROC head and neck and spine plans involved similar amounts of complexity and were largely distinct from the prostate and lung plans, but no further distinctions could be made. Plan complexity in the head and neck and spine IROC phantom plans were similar to the complexity encountered in the UW clinical plans. Conclusions: There is substantial variability in plan complexity between institutions when planning for the same objective. For each IROC anatomical phantom treated, the magnitude of variability in plan complexity between institutions is similar to the variability in plan complexity encountered within a single institution database containing several hundred unique clinical plans treating corresponding anatomies in actual patients. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/mp.17086 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 5693 Subjects: – SubjectFull: Principal components analysis Type: general – SubjectFull: Databases Type: general – SubjectFull: Planning techniques Type: general – SubjectFull: Radiotherapy Type: general – SubjectFull: Quality assurance Type: general – SubjectFull: Lungs Type: general Titles: – TitleFull: Multi‐institution single geometry plan complexity characteristics based on IROC phantoms. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Desai, Vimal – PersonEntity: Name: NameFull: Labby, Zacariah – PersonEntity: Name: NameFull: Culberson, Wesley – PersonEntity: Name: NameFull: DeWerd, Larry – PersonEntity: Name: NameFull: Kry, Stephen IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 51 – Type: issue Value: 8 Titles: – TitleFull: Medical Physics Type: main |
| ResultId | 1 |
