Auto-analysis intrafraction prostate movement patterns based on transperineal ultrasound real-time tracking system and linear discriminant model
10.3760/cma.j.cn113030-20200108-00011
- VernacularTitle:基于实时超声图像引导技术和线性判别模型分析前列腺癌放疗分次内运动模式
- Author:
Yan GAO
1
;
Bo ZHAO
;
Xianshu GAO
;
Xin QI
Author Information
1. 北京大学第一医院放疗科 100034
- From:
Chinese Journal of Radiation Oncology
2020;29(6):455-460
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To qualitatively analyze and auto-discriminate intrafraction prostate movement patterns based on transperineal ultrasound (TPUS) technique and linear discriminant analysis (LDA) method, which lays a solid foundation for individualized precise radiotherapy.Methods:A total of 1265 intrafraction motion trajectories and millions of monitoring data were recorded by TPUS from 61 prostate cancer patients. Seven typical patterns were considered: stable at baseline, slight transient excursion, persistent excursion and continuous drift, as well as obvious transient excursion, persistent excursion and continuous drift. Motion trajectory diagrams associated with the displacement-time function were generated by MATLAB programming. Valuable features were selected and different patterns were identified. Discriminant accuracy and receiver operating characteristic (ROC) curve were utilized to evaluate the performance of LDA model.Results:Four movement patterns were found per patient during the whole treatment process, and unstable type was occupied at (35.00±21.49)%. With the increase of treatment times, motion trajectories did not show an increasingly stable trend, and the appearance of different patterns was extremely irregular. After quantitative analysis, discriminant accuracy of LDA method was 90.4% for the training set and 89.5% for the testing set, with a sensitivity of 84.9% and a specificity of 91.1%.Conclusion:Intrafraction movement patterns are characterized by diversity and randomness. The LDA method can be used to discriminate different movement patterns effectively, and the unknown samples can be identified by discriminant equations and centroid coordinates.
