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Article: Reporting and analyzing statistical uncertainties in Monte Carlo-based treatment planning

TitleReporting and analyzing statistical uncertainties in Monte Carlo-based treatment planning
Authors
KeywordsLung cancer
Dose distributions
Monte Carlo-based treatment planning
Uncertainty volume histograms
Statistical uncertainties
Issue Date2006
Citation
International Journal of Radiation Oncology Biology Physics, 2006, v. 65, n. 4, p. 1249-1259 How to Cite?
AbstractPurpose: To investigate methods of reporting and analyzing statistical uncertainties in doses to targets and normal tissues in Monte Carlo (MC)-based treatment planning. Methods and Materials: Methods for quantifying statistical uncertainties in dose, such as uncertainty specification to specific dose points, or to volume-based regions, were analyzed in MC-based treatment planning for 5 lung cancer patients. The effect of statistical uncertainties on target and normal tissue dose indices was evaluated. The concept of uncertainty volume histograms for targets and organs at risk was examined, along with its utility, in conjunction with dose volume histograms, in assessing the acceptability of the statistical precision in dose distributions. The uncertainty evaluation tools were extended to four-dimensional planning for application on multiple instances of the patient geometry. All calculations were performed using the Dose Planning Method MC code. Results: For targets, generalized equivalent uniform doses and mean target doses converged at 150 million simulated histories, corresponding to relative uncertainties of less than 2% in the mean target doses. For the normal lung tissue (a volume-effect organ), mean lung dose and normal tissue complication probability converged at 150 million histories despite the large range in the relative organ uncertainty volume histograms. For "serial" normal tissues such as the spinal cord, large fluctuations exist in point dose relative uncertainties. Conclusions: The tools presented here provide useful means for evaluating statistical precision in MC-based dose distributions. Tradeoffs between uncertainties in doses to targets, volume-effect organs, and "serial" normal tissues must be considered carefully in determining acceptable levels of statistical precision in MC-computed dose distributions. © 2006 Elsevier Inc. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/266864
ISSN
2023 Impact Factor: 6.4
2023 SCImago Journal Rankings: 1.992
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChetty, Indrin J.-
dc.contributor.authorRosu, Mihaela-
dc.contributor.authorKessler, Marc L.-
dc.contributor.authorFraass, Benedick A.-
dc.contributor.authorTen Haken, Randall K.-
dc.contributor.authorKong, Feng Ming (Spring)-
dc.contributor.authorMcShan, Daniel L.-
dc.date.accessioned2019-01-31T07:19:50Z-
dc.date.available2019-01-31T07:19:50Z-
dc.date.issued2006-
dc.identifier.citationInternational Journal of Radiation Oncology Biology Physics, 2006, v. 65, n. 4, p. 1249-1259-
dc.identifier.issn0360-3016-
dc.identifier.urihttp://hdl.handle.net/10722/266864-
dc.description.abstractPurpose: To investigate methods of reporting and analyzing statistical uncertainties in doses to targets and normal tissues in Monte Carlo (MC)-based treatment planning. Methods and Materials: Methods for quantifying statistical uncertainties in dose, such as uncertainty specification to specific dose points, or to volume-based regions, were analyzed in MC-based treatment planning for 5 lung cancer patients. The effect of statistical uncertainties on target and normal tissue dose indices was evaluated. The concept of uncertainty volume histograms for targets and organs at risk was examined, along with its utility, in conjunction with dose volume histograms, in assessing the acceptability of the statistical precision in dose distributions. The uncertainty evaluation tools were extended to four-dimensional planning for application on multiple instances of the patient geometry. All calculations were performed using the Dose Planning Method MC code. Results: For targets, generalized equivalent uniform doses and mean target doses converged at 150 million simulated histories, corresponding to relative uncertainties of less than 2% in the mean target doses. For the normal lung tissue (a volume-effect organ), mean lung dose and normal tissue complication probability converged at 150 million histories despite the large range in the relative organ uncertainty volume histograms. For "serial" normal tissues such as the spinal cord, large fluctuations exist in point dose relative uncertainties. Conclusions: The tools presented here provide useful means for evaluating statistical precision in MC-based dose distributions. Tradeoffs between uncertainties in doses to targets, volume-effect organs, and "serial" normal tissues must be considered carefully in determining acceptable levels of statistical precision in MC-computed dose distributions. © 2006 Elsevier Inc. All rights reserved.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Radiation Oncology Biology Physics-
dc.subjectLung cancer-
dc.subjectDose distributions-
dc.subjectMonte Carlo-based treatment planning-
dc.subjectUncertainty volume histograms-
dc.subjectStatistical uncertainties-
dc.titleReporting and analyzing statistical uncertainties in Monte Carlo-based treatment planning-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.ijrobp.2006.03.039-
dc.identifier.pmid16798417-
dc.identifier.scopuseid_2-s2.0-33745184007-
dc.identifier.volume65-
dc.identifier.issue4-
dc.identifier.spage1249-
dc.identifier.epage1259-
dc.identifier.isiWOS:000238878800039-
dc.identifier.issnl0360-3016-

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