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Development of a neuro-fuzzy technique for automated parameter optimization of inverse treatment planning.

Publication ,  Journal Article
Stieler, F; Yan, H; Lohr, F; Wenz, F; Yin, F-F
Published in: Radiat Oncol
September 25, 2009

BACKGROUND: Parameter optimization in the process of inverse treatment planning for intensity modulated radiation therapy (IMRT) is mainly conducted by human planners in order to create a plan with the desired dose distribution. To automate this tedious process, an artificial intelligence (AI) guided system was developed and examined. METHODS: The AI system can automatically accomplish the optimization process based on prior knowledge operated by several fuzzy inference systems (FIS). Prior knowledge, which was collected from human planners during their routine trial-and-error process of inverse planning, has first to be "translated" to a set of "if-then rules" for driving the FISs. To minimize subjective error which could be costly during this knowledge acquisition process, it is necessary to find a quantitative method to automatically accomplish this task. A well-developed machine learning technique, based on an adaptive neuro fuzzy inference system (ANFIS), was introduced in this study. Based on this approach, prior knowledge of a fuzzy inference system can be quickly collected from observation data (clinically used constraints). The learning capability and the accuracy of such a system were analyzed by generating multiple FIS from data collected from an AI system with known settings and rules. RESULTS: Multiple analyses showed good agreements of FIS and ANFIS according to rules (error of the output values of ANFIS based on the training data from FIS of 7.77 +/- 0.02%) and membership functions (3.9%), thus suggesting that the "behavior" of an FIS can be propagated to another, based on this process. The initial experimental results on a clinical case showed that ANFIS is an effective way to build FIS from practical data, and analysis of ANFIS and FIS with clinical cases showed good planning results provided by ANFIS. OAR volumes encompassed by characteristic percentages of isodoses were reduced by a mean of between 0 and 28%. CONCLUSION: The study demonstrated a feasible way to automatically perform parameter optimization of inverse treatment planning under guidance of prior knowledge without human intervention other than providing a set of constraints that have proven clinically useful in a given setting.

Duke Scholars

Published In

Radiat Oncol

DOI

EISSN

1748-717X

Publication Date

September 25, 2009

Volume

4

Start / End Page

39

Location

England

Related Subject Headings

  • Radiotherapy Planning, Computer-Assisted
  • Radiotherapy Dosage
  • Oncology & Carcinogenesis
  • Neoplasms
  • Humans
  • Fuzzy Logic
  • Artificial Intelligence
  • Algorithms
  • 3211 Oncology and carcinogenesis
  • 3202 Clinical sciences
 

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Stieler, F., Yan, H., Lohr, F., Wenz, F., & Yin, F.-F. (2009). Development of a neuro-fuzzy technique for automated parameter optimization of inverse treatment planning. Radiat Oncol, 4, 39. https://doi.org/10.1186/1748-717X-4-39
Stieler, Florian, Hui Yan, Frank Lohr, Frederik Wenz, and Fang-Fang Yin. “Development of a neuro-fuzzy technique for automated parameter optimization of inverse treatment planning.Radiat Oncol 4 (September 25, 2009): 39. https://doi.org/10.1186/1748-717X-4-39.
Stieler F, Yan H, Lohr F, Wenz F, Yin F-F. Development of a neuro-fuzzy technique for automated parameter optimization of inverse treatment planning. Radiat Oncol. 2009 Sep 25;4:39.
Stieler, Florian, et al. “Development of a neuro-fuzzy technique for automated parameter optimization of inverse treatment planning.Radiat Oncol, vol. 4, Sept. 2009, p. 39. Pubmed, doi:10.1186/1748-717X-4-39.
Stieler F, Yan H, Lohr F, Wenz F, Yin F-F. Development of a neuro-fuzzy technique for automated parameter optimization of inverse treatment planning. Radiat Oncol. 2009 Sep 25;4:39.
Journal cover image

Published In

Radiat Oncol

DOI

EISSN

1748-717X

Publication Date

September 25, 2009

Volume

4

Start / End Page

39

Location

England

Related Subject Headings

  • Radiotherapy Planning, Computer-Assisted
  • Radiotherapy Dosage
  • Oncology & Carcinogenesis
  • Neoplasms
  • Humans
  • Fuzzy Logic
  • Artificial Intelligence
  • Algorithms
  • 3211 Oncology and carcinogenesis
  • 3202 Clinical sciences