Very Fast Simulated Reannealing in radiation therapy treatment plan optimization

Steven M. Morrill, Kam Shing Lam, Richard G. Lane, Mark Langer, Isaac I. Rosen

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

Purpose: Very Fast Simulated Reannealing is a relatively new (1989) abd sophisticated algorithm for simulated annealing applications. It offers the advantages of annealing methods while requiring shorter execution times. The purpose of this investigation was to adapt Very Fast Simulated Reannealing to conformal treatment planning optimization. Methods and Materials: We used Very Fast Simulated Reannealing to optimize treatment for three clinical cases with two different cost functions. The first cost function was linear (minimum target dose) with nonlinear dose-volume weighted product of normal tissue complication probabilities and the tumor control probability. Results: For the cost functions used in this study, the Very Fast Simulated Reanneling algorithm achieved results within 5-1% of the final solution (100,000 iterations) after 1000 iterations and within 3-5% of the final solution after 5000-10000 iterations. These solutions were superior to those produced by a conventional treatment plan based on an analysis of the resulting dose-volume histograms. However, this technique is a stochastic method and results vary in a statistical manner. Successive solutions may differ by up to 10%. Conclusion: Very Fast Reannealing with modifications, is suitable for radiation therapy treatment planning optimization. It produced results within 3-10% of the optimal solution, produced using another optimization algorithm (Mixed Integer Programming), in clinically useful execution times.

Original languageEnglish (US)
Pages (from-to)179-188
Number of pages10
JournalInternational Journal of Radiation Oncology Biology Physics
Volume31
Issue number1
DOIs
StatePublished - Jan 1 1995
Externally publishedYes

Fingerprint

radiation therapy
Radiotherapy
iteration
optimization
costs
Costs and Cost Analysis
dosage
planning
simulated annealing
Therapeutics
programming
histograms
integers
tumors
annealing
products
Neoplasms

Keywords

  • Optimization
  • Simulated annealing
  • Treatment planning

ASJC Scopus subject areas

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

Cite this

Very Fast Simulated Reannealing in radiation therapy treatment plan optimization. / Morrill, Steven M.; Lam, Kam Shing; Lane, Richard G.; Langer, Mark; Rosen, Isaac I.

In: International Journal of Radiation Oncology Biology Physics, Vol. 31, No. 1, 01.01.1995, p. 179-188.

Research output: Contribution to journalArticle

Morrill, Steven M. ; Lam, Kam Shing ; Lane, Richard G. ; Langer, Mark ; Rosen, Isaac I. / Very Fast Simulated Reannealing in radiation therapy treatment plan optimization. In: International Journal of Radiation Oncology Biology Physics. 1995 ; Vol. 31, No. 1. pp. 179-188.
@article{cf07997c73094338aeac8ecb8e845376,
title = "Very Fast Simulated Reannealing in radiation therapy treatment plan optimization",
abstract = "Purpose: Very Fast Simulated Reannealing is a relatively new (1989) abd sophisticated algorithm for simulated annealing applications. It offers the advantages of annealing methods while requiring shorter execution times. The purpose of this investigation was to adapt Very Fast Simulated Reannealing to conformal treatment planning optimization. Methods and Materials: We used Very Fast Simulated Reannealing to optimize treatment for three clinical cases with two different cost functions. The first cost function was linear (minimum target dose) with nonlinear dose-volume weighted product of normal tissue complication probabilities and the tumor control probability. Results: For the cost functions used in this study, the Very Fast Simulated Reanneling algorithm achieved results within 5-1{\%} of the final solution (100,000 iterations) after 1000 iterations and within 3-5{\%} of the final solution after 5000-10000 iterations. These solutions were superior to those produced by a conventional treatment plan based on an analysis of the resulting dose-volume histograms. However, this technique is a stochastic method and results vary in a statistical manner. Successive solutions may differ by up to 10{\%}. Conclusion: Very Fast Reannealing with modifications, is suitable for radiation therapy treatment planning optimization. It produced results within 3-10{\%} of the optimal solution, produced using another optimization algorithm (Mixed Integer Programming), in clinically useful execution times.",
keywords = "Optimization, Simulated annealing, Treatment planning",
author = "Morrill, {Steven M.} and Lam, {Kam Shing} and Lane, {Richard G.} and Mark Langer and Rosen, {Isaac I.}",
year = "1995",
month = "1",
day = "1",
doi = "10.1016/0360-3016(94)00350-T",
language = "English (US)",
volume = "31",
pages = "179--188",
journal = "International Journal of Radiation Oncology Biology Physics",
issn = "0360-3016",
publisher = "Elsevier Inc.",
number = "1",

}

TY - JOUR

T1 - Very Fast Simulated Reannealing in radiation therapy treatment plan optimization

AU - Morrill, Steven M.

AU - Lam, Kam Shing

AU - Lane, Richard G.

AU - Langer, Mark

AU - Rosen, Isaac I.

PY - 1995/1/1

Y1 - 1995/1/1

N2 - Purpose: Very Fast Simulated Reannealing is a relatively new (1989) abd sophisticated algorithm for simulated annealing applications. It offers the advantages of annealing methods while requiring shorter execution times. The purpose of this investigation was to adapt Very Fast Simulated Reannealing to conformal treatment planning optimization. Methods and Materials: We used Very Fast Simulated Reannealing to optimize treatment for three clinical cases with two different cost functions. The first cost function was linear (minimum target dose) with nonlinear dose-volume weighted product of normal tissue complication probabilities and the tumor control probability. Results: For the cost functions used in this study, the Very Fast Simulated Reanneling algorithm achieved results within 5-1% of the final solution (100,000 iterations) after 1000 iterations and within 3-5% of the final solution after 5000-10000 iterations. These solutions were superior to those produced by a conventional treatment plan based on an analysis of the resulting dose-volume histograms. However, this technique is a stochastic method and results vary in a statistical manner. Successive solutions may differ by up to 10%. Conclusion: Very Fast Reannealing with modifications, is suitable for radiation therapy treatment planning optimization. It produced results within 3-10% of the optimal solution, produced using another optimization algorithm (Mixed Integer Programming), in clinically useful execution times.

AB - Purpose: Very Fast Simulated Reannealing is a relatively new (1989) abd sophisticated algorithm for simulated annealing applications. It offers the advantages of annealing methods while requiring shorter execution times. The purpose of this investigation was to adapt Very Fast Simulated Reannealing to conformal treatment planning optimization. Methods and Materials: We used Very Fast Simulated Reannealing to optimize treatment for three clinical cases with two different cost functions. The first cost function was linear (minimum target dose) with nonlinear dose-volume weighted product of normal tissue complication probabilities and the tumor control probability. Results: For the cost functions used in this study, the Very Fast Simulated Reanneling algorithm achieved results within 5-1% of the final solution (100,000 iterations) after 1000 iterations and within 3-5% of the final solution after 5000-10000 iterations. These solutions were superior to those produced by a conventional treatment plan based on an analysis of the resulting dose-volume histograms. However, this technique is a stochastic method and results vary in a statistical manner. Successive solutions may differ by up to 10%. Conclusion: Very Fast Reannealing with modifications, is suitable for radiation therapy treatment planning optimization. It produced results within 3-10% of the optimal solution, produced using another optimization algorithm (Mixed Integer Programming), in clinically useful execution times.

KW - Optimization

KW - Simulated annealing

KW - Treatment planning

UR - http://www.scopus.com/inward/record.url?scp=0028820966&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0028820966&partnerID=8YFLogxK

U2 - 10.1016/0360-3016(94)00350-T

DO - 10.1016/0360-3016(94)00350-T

M3 - Article

VL - 31

SP - 179

EP - 188

JO - International Journal of Radiation Oncology Biology Physics

JF - International Journal of Radiation Oncology Biology Physics

SN - 0360-3016

IS - 1

ER -