Comprehensive index combining plan complexity characteristics and dosimetric evaluation indicators to improve model performance for predicting the results of dose verification(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2022年第4期
- Page:
- 409-414
- Research Field:
- 医学放射物理
- Publishing date:
Info
- Title:
- Comprehensive index combining plan complexity characteristics and dosimetric evaluation indicators to improve model performance for predicting the results of dose verification
- Author(s):
- SHEN Luyao1; WEI Qianglin1; ZHANG Junjun2; BIN Shizhen2; LIU Yibao1
- 1. School of Nuclear Science and Engineering, East China University of Technology, Nanchang 330013, China 2. Department of Oncology, the Third Xiangya Hospital of Central South University, Changsha 410013, China
- Keywords:
- Keywords: machine learning dose verification plan complexity characteristic dosimetric evaluation indicator random forest
- PACS:
- R318；R811.1
- DOI:
- DOI:10.3969/j.issn.1005-202X.2022.04.003
- Abstract:
- Abstract: Objective To develop a random forest model for predicting the results of intensity-modulated radiotherapy (IMRT) plan dose verification, and to study the feasibility of improving model performance by integrating plan complexity characteristics and dosimetric evaluation indicators. Methods Electronic portal imaging device was used for the dose verification of 269 IMRT plans with a total of 2 558 fields. The threshold of gamma passing rate (2%/2 mm criterion) was 95%, and there were only two possible outcomes in dose verification, namely pass and fail. The dosimetric evaluation indicators of plans and the complexity characteristics of the radiation fields were extracted for constructing the dose model (based on dosimetric evaluation indicators), planning model (based on plan complexity characteristics) and the hybrid model (comprehensively considering dosimetric evaluation indicators and plan complexity characteristics). The performances of the prediction models were evaluated by AUC, specificity and sensitivity. Results The AUC of the dose model, the planning model and the hybrid model were 0.68, 0.80 and 0.82, respectively, and the hybrid model had the highest AUC. The specificity and sensitivity of the hybrid model were 0.70 and 0.79, both higher than those of the other two models. The number of samples required for the optimal performance of the dose model the planning model and hybrid model were 1 200, 900 and 700, respectively. Conclusion Comprehensively considering dosimetric evaluation indicators and plan complexity characteristics can improve the prediction performance of the model, and at the same time make up for the lack of sample size to a certain extent, providing a reference for improvement of the performance of machine learning model for predicting the results of dose verification.
Last Update: 2022-04-27