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


CONCLUSIONS
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.
Yan
Hui
H
Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA. hui.yan@duke.edu
Yin
Fang-Fang
FF
Willett
Christopher
C
D001185:Artificial Intelligence; D001932:Brain Neoplasms / Q000532:radiotherapy; D017143:Fuzzy Logic; D006258:Head and Neck Neoplasms / Q000532:radiotherapy; D006801:Humans; D008297:Male; D011471:Prostatic Neoplasms / Q000532:radiotherapy; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted; D011881:Radiotherapy, Computer-Assisted; D013120:Spinal Cord Neoplasms / Q000532:radiotherapy