Comparison of radiobiological models for evaluation of radiotherapy treatment planning of breast cancer
- VernacularTitle:放射生物模型在乳腺癌放疗计划评价中的比较
- Author:
Weibing ZHOU
;
Yan FENG
;
Jiayi CHEN
;
Zhen ZHANG
- Publication Type:Journal Article
- Keywords:
Breast neoplasms;
Radiobiological models;
Comparison analysis
- From:
Chinese Journal of Radiation Oncology
2008;17(4):293-297
- CountryChina
- Language:Chinese
-
Abstract:
Objective To find an appropriate r3diobiological model for analyzing the biological effect of the radiotherapy for breast cancer by comparing different results computed by various types of radiobiological models. Methods DVHs database simulating breast conserving radiotherapy was set up,based on clinical DVHs data of the heart.the lung and PTV of 22 patients with early breast cancer given conventional tangential radiotherapy.Two models assessing NTCP of radiation pneumonitis and cardiac mortality and four models assessing TCP were compared by analyzing the effects of the parameters and DVH database input methods on the results. Results When mean irradiation dose of the whole lung was 30 Gy.the incidence of radiation pneumonitis was 32%and 54%predicted by NTCP-RSM model and NTCP-Lyman model,respectively.When 1%cardiac mortality of radiation was assumed,28 Gy and 40 Gy isodose should cover the heart assessed by the two models.The mean TCP were 21.1%.80.8%.38.4%and 41.0%when assessed by LQ-Poisson-TCP,Zaider-TCP,Poisson-TCP and Logit-TCP models,respectively.Setting various differential DVH(dDVH)bins had very few effect on the NTCP/TCP results in a certain model.Adopting physical dose or LQED2 affected the results with greater resu]ts for physical dose.Variation in α or β value,tumor cell density and D50 had significant effect upon TCP results in LQ-Poisson-TCP(P:0.000). Conclusions NTCP-Lyman model is better for predicting the incidence of radiation pneumonitis while NTCP-RSM model is better for predicting radiation-induced cardiac mortality.LQ-Poisson-TCP is the best TCP model for clinical application.Parameters selected for model can significantly affect the results.It is imporrant to understand the distinct characteristics of different models.