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

Indrin J. Chetty, Mihaela Rosu, Marc L. Kessler, Benedick A. Fraass, Randall K. Ten Haken, Feng Ming (Spring) Kong, Daniel L. McShan

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

Purpose: 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.

Original languageEnglish (US)
Pages (from-to)1249-1259
Number of pages11
JournalInternational Journal of Radiation Oncology Biology Physics
Volume65
Issue number4
DOIs
StatePublished - Jul 15 2006

Fingerprint

Uncertainty
planning
dosage
Organ Size
Therapeutics
organs
histograms
lungs
Organs at Risk
Monte Carlo Method
Lung
histories
Lung Neoplasms
spinal cord
Spinal Cord
tradeoffs
acceptability
Monte Carlo method
specifications
cancer

Keywords

  • Dose distributions
  • Lung cancer
  • Monte Carlo-based treatment planning
  • Statistical uncertainties
  • Uncertainty volume histograms

ASJC Scopus subject areas

  • Radiation
  • Oncology
  • Radiology Nuclear Medicine and imaging
  • Cancer Research

Cite this

Chetty, I. J., Rosu, M., Kessler, M. L., Fraass, B. A., Ten Haken, R. K., Kong, F. M. S., & McShan, D. L. (2006). Reporting and analyzing statistical uncertainties in Monte Carlo-based treatment planning. International Journal of Radiation Oncology Biology Physics, 65(4), 1249-1259. https://doi.org/10.1016/j.ijrobp.2006.03.039

Reporting and analyzing statistical uncertainties in Monte Carlo-based treatment planning. / Chetty, Indrin J.; Rosu, Mihaela; Kessler, Marc L.; Fraass, Benedick A.; Ten Haken, Randall K.; Kong, Feng Ming (Spring); McShan, Daniel L.

In: International Journal of Radiation Oncology Biology Physics, Vol. 65, No. 4, 15.07.2006, p. 1249-1259.

Research output: Contribution to journalArticle

Chetty, Indrin J. ; Rosu, Mihaela ; Kessler, Marc L. ; Fraass, Benedick A. ; Ten Haken, Randall K. ; Kong, Feng Ming (Spring) ; McShan, Daniel L. / Reporting and analyzing statistical uncertainties in Monte Carlo-based treatment planning. In: International Journal of Radiation Oncology Biology Physics. 2006 ; Vol. 65, No. 4. pp. 1249-1259.
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