Spatial mapping of the biologic effectiveness of scanned particle beams: towards biologically optimized particle therapy Article Swipe
Fada Guan
,
Lawrence F. Bronk
,
U Titt
,
Steven H. Lin
,
Dragan Mirković
,
Matthew D. Kerr
,
X Zhu
,
Jeffrey Dinh
,
Mary Sobieski
,
Clifford Stephan
,
C Peeler
,
Reza Taleei
,
Radhe Mohan
,
David R. Grosshans
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1038/srep09850
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1038/srep09850
The physical properties of particles used in radiation therapy, such as protons, have been well characterized and their dose distributions are superior to photon-based treatments. However, proton therapy may also have inherent biologic advantages that have not been capitalized on. Unlike photon beams, the linear energy transfer (LET) and hence biologic effectiveness of particle beams varies along the beam path. Selective placement of areas of high effectiveness could enhance tumor cell kill and simultaneously spare normal tissues. However, previous methods for mapping spatial variations in biologic effectiveness are time-consuming and often yield inconsistent results with large uncertainties. Thus the data needed to accurately model relative biological effectiveness to guide novel treatment planning approaches are limited. We used Monte Carlo modeling and high-content automated clonogenic survival assays to spatially map the biologic effectiveness of scanned proton beams with high accuracy and throughput while minimizing biological uncertainties. We found that the relationship between cell kill, dose and LET, is complex and non-unique. Measured biologic effects were substantially greater than in most previous reports, and non-linear surviving fraction response was observed even for the highest LET values. Extension of this approach could generate data needed to optimize proton therapy plans incorporating variable RBE.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/srep09850
- https://www.nature.com/articles/srep09850.pdf
- OA Status
- gold
- Cited By
- 160
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2266637648
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2266637648Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/srep09850Digital Object Identifier
- Title
-
Spatial mapping of the biologic effectiveness of scanned particle beams: towards biologically optimized particle therapyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-05-18Full publication date if available
- Authors
-
Fada Guan, Lawrence F. Bronk, U Titt, Steven H. Lin, Dragan Mirković, Matthew D. Kerr, X Zhu, Jeffrey Dinh, Mary Sobieski, Clifford Stephan, C Peeler, Reza Taleei, Radhe Mohan, David R. GrosshansList of authors in order
- Landing page
-
https://doi.org/10.1038/srep09850Publisher landing page
- PDF URL
-
https://www.nature.com/articles/srep09850.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.nature.com/articles/srep09850.pdfDirect OA link when available
- Concepts
-
Linear energy transfer, Proton therapy, Relative biological effectiveness, Particle therapy, Monte Carlo method, Computer science, Proton, Biological system, Bragg peak, Physics, Nuclear medicine, Beam (structure), Radiation, Mathematics, Statistics, Medicine, Biology, Optics, Nuclear physicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
160Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 16, 2024: 16, 2023: 6, 2022: 14, 2021: 14Per-year citation counts (last 5 years)
- References (count)
-
60Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| countries_distinct_count | 1 |
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| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.49000000953674316 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
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| citation_normalized_percentile.is_in_top_10_percent | True |