Skip to main content
Journal cover image

Evaluation of an artificial intelligence guided inverse planning system: clinical case study.

Publication ,  Journal Article
Yan, H; Yin, F-F; Willett, C
Published in: Radiother Oncol
April 2007

PURPOSE: An artificial intelligence (AI) guided method for parameter adjustment of inverse planning was implemented on a commercial inverse treatment planning system. For evaluation purpose, four typical clinical cases were tested and the results from both plans achieved by automated and manual methods were compared. METHODS AND MATERIALS: The procedure of parameter adjustment mainly consists of three major loops. Each loop is in charge of modifying parameters of one category, which is carried out by a specially customized fuzzy inference system. A physician prescribed multiple constraints for a selected volume were adopted to account for the tradeoff between prescription dose to the PTV and dose-volume constraints for critical organs. The searching process for an optimal parameter combination began with the first constraint, and proceeds to the next until a plan with acceptable dose was achieved. The initial setup of the plan parameters was the same for each case and was adjusted independently by both manual and automated methods. After the parameters of one category were updated, the intensity maps of all fields were re-optimized and the plan dose was subsequently re-calculated. When final plan arrived, the dose statistics were calculated from both plans and compared. RESULTS: For planned target volume (PTV), the dose for 95% volume is up to 10% higher in plans using the automated method than those using the manual method. For critical organs, an average decrease of the plan dose was achieved. However, the automated method cannot improve the plan dose for some critical organs due to limitations of the inference rules currently employed. For normal tissue, there was no significant difference between plan doses achieved by either automated or manual method. CONCLUSION: With the application of AI-guided method, the basic parameter adjustment task can be accomplished automatically and a comparable plan dose was achieved in comparison with that achieved by the manual method. Future improvements to incorporate case-specific inference rules are essential to fully automate the inverse planning process.

Duke Scholars

Published In

Radiother Oncol

DOI

ISSN

0167-8140

Publication Date

April 2007

Volume

83

Issue

1

Start / End Page

76 / 85

Location

Ireland

Related Subject Headings

  • Spinal Cord Neoplasms
  • Radiotherapy, Computer-Assisted
  • Radiotherapy Planning, Computer-Assisted
  • Radiotherapy Dosage
  • Prostatic Neoplasms
  • Oncology & Carcinogenesis
  • Male
  • Humans
  • Head and Neck Neoplasms
  • Fuzzy Logic
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Yan, H., Yin, F.-F., & Willett, C. (2007). Evaluation of an artificial intelligence guided inverse planning system: clinical case study. Radiother Oncol, 83(1), 76–85. https://doi.org/10.1016/j.radonc.2007.02.013
Yan, Hui, Fang-Fang Yin, and Christopher Willett. “Evaluation of an artificial intelligence guided inverse planning system: clinical case study.Radiother Oncol 83, no. 1 (April 2007): 76–85. https://doi.org/10.1016/j.radonc.2007.02.013.
Yan H, Yin F-F, Willett C. Evaluation of an artificial intelligence guided inverse planning system: clinical case study. Radiother Oncol. 2007 Apr;83(1):76–85.
Yan, Hui, et al. “Evaluation of an artificial intelligence guided inverse planning system: clinical case study.Radiother Oncol, vol. 83, no. 1, Apr. 2007, pp. 76–85. Pubmed, doi:10.1016/j.radonc.2007.02.013.
Yan H, Yin F-F, Willett C. Evaluation of an artificial intelligence guided inverse planning system: clinical case study. Radiother Oncol. 2007 Apr;83(1):76–85.
Journal cover image

Published In

Radiother Oncol

DOI

ISSN

0167-8140

Publication Date

April 2007

Volume

83

Issue

1

Start / End Page

76 / 85

Location

Ireland

Related Subject Headings

  • Spinal Cord Neoplasms
  • Radiotherapy, Computer-Assisted
  • Radiotherapy Planning, Computer-Assisted
  • Radiotherapy Dosage
  • Prostatic Neoplasms
  • Oncology & Carcinogenesis
  • Male
  • Humans
  • Head and Neck Neoplasms
  • Fuzzy Logic