Personalized quantitative evaluation of the quality of radiotherapy plans based on dose prediction
10.3760/cma.j.cn112271-20211122-00459
- VernacularTitle:基于剂量预测的个性化放射治疗计划质量的定量评价方法
- Author:
Bingzhi WU
1
;
Zhao PENG
;
Yongheng YAN
;
Jieping ZHOU
;
Xie XU
;
Xi PEI
Author Information
1. 中国科学技术大学核科学技术学院，合肥 230025
- Keywords:
Intensity Modulation Radiation Therapy;
Plan quality evaluation;
Dose prediction
- From:
Chinese Journal of Radiological Medicine and Protection
2022;42(3):188-193
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop a dose prediction-based quantitative evaluation method of the quality of radiotherapy plans, and to verify the clinical feasibility and clinical value of the method .Methods:The 3D U-Netwas trained using the radiotherapy plans of 45 rectal cancer cases that were formulated by physicists with more than five years of radiotherapy experience. After obtaining 3D dose distribution using 3D U-Net prediction, this study established the plan quality metrics of intensity modulated radiotherapy(IMRT) rectal cancer radiotherapy plans using dose-volume histogram(DVH) indexes of dose prediction. Then, the initial scores of rectal cancer radiotherapy plans were determined.Taking the predicted dose as the optimization goal, the radiotherapy plans were optimized and scored again. The clinical significance of this scoring method was verified by comparing the scores and dosimetric parameters of the 15 rectal cancer cases before and after optimization.Results:The radiotherapy plans before and after optimization all met the clinical dose requirements. The total scores were(77.21±9.74) before optimization, and (88.78±4.92) after optimization. Therefore, the optimized radiotherapy planswon increased scores with a statistically significant difference( t=-4.105, P<0.05). Compared to the plans before optimization, the optimized plans show decreased Dmax of all organs at risk to different extents. Moreover, the Dmax, V107%, and HI of PTV and the Dmax of the bladder decreased in the optimized plans, with statistically significant differences ( t=2.346-5.771, P<0.05). There was no statistically significant difference in other indexes before and after optimization ( P>0.05).The quality of the optimized plans were improved to a certain extent. Conclusions:This study proposed a dose prediction-based quantitative evaluation method of the quality of radiotherapy plans. It can be used for the effective personalized elevation of the quality of radiotherapy plans, which is beneficial to effectively compare and review the quality of clinical plans determined by different physicists and provide personalized dose indicators. Moreover, it can provide great guidance for the formulation of clinical therapy plans.
