Abstract： Objective To investigate the clinical effect of lung equivalent uniform dose (LEUD)-based predictive model for radiation pneumonitis (RP) induced by volumetric modulated arc therapy (VMAT) and to determine the optimal a value. Methods A total of 65 patients with primary lung cancer who received VMAT from July 2015 to February 2016 were divided into RP group and non-RP group according to the presence or absence of RP after radiotherapy. Their dose-volume histogram (DVH) data and other data were obtained and analyzed by the self-compiled numerical analysis program. The LEUD values in the two groups were calculated at a=[-50, 50], and then the a value was identified when the relative difference of LEUD between the two groups was maximal. The paired t test was used for analyzing the differences in V5, V20, V30, minimum lethal dose (MLD), and LEUD (aoptimal) between the two groups. A Pearson correlation analysis was used to determine the correlation of Vdose and LEUD (aoptimal) with RP. The logistic regression method was used to establish the predictive model of RP. Results The maximum relative difference in LEUD between RP group and non-RP group was obtained at a=0.3(627.94 cGy vs. 510.23 cGy, relative difference[R]=23.07%). R decreased slowly at t=[-50,-5], increased sharply at t=[-5, 0], and reached the maximum value at a=0.3. After a rapid decrease at a=[0.3, 4], R decreased slowly at a=[4, 50]. The correlation analysis of the traditional physical volume dose threshold also showed thatthe LEUD (at a=0.3) was correlated with V5, V10, V20, and MLD (r=0.929, P<0.05). Conclusions For patients receiving VMAT for thoracic cancer, LEUD (at a=0.3) can distinguish between patients with and without RP. Therefore, LEUD is recommended to be<510 cGy. A combination of LEUD and conventional physical dose has a good clinical predictive value for RP under non-uniform irradiation.
Wang Cheng,Gu Jiale,Deng Qinghua et al. Clinical value of lung equivalent uniform dose in predicting VMAT-induced radiation pneumonitis[J]. Chinese Journal of Radiation Oncology, 2017, 26(7): 749-753.
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