Xu Zhi-Yong, Liang Shi-Xiong, Zhu Ji, Zhu Xiao-Dong, Zhao Jian-Dong, Lu Hai-Jie, Yang Yun-Li, Chen Long, Wang An-Yu, Fu Xiao-Long, Jiang Guo-Liang
Department of Radiation Oncology, Fudan University Cancer Hospital, Shanghai, China.
Int J Radiat Oncol Biol Phys. 2006 May 1;65(1):189-95. doi: 10.1016/j.ijrobp.2005.11.034. Epub 2006 Mar 20.
To describe the probability of RILD by application of the Lyman-Kutcher-Burman normal-tissue complication (NTCP) model for primary liver carcinoma (PLC) treated with hypofractionated three-dimensional conformal radiotherapy (3D-CRT).
A total of 109 PLC patients treated by 3D-CRT were followed for RILD. Of these patients, 93 were in liver cirrhosis of Child-Pugh Grade A, and 16 were in Child-Pugh Grade B. The Michigan NTCP model was used to predict the probability of RILD, and then the modified Lyman NTCP model was generated for Child-Pugh A and Child-Pugh B patients by maximum-likelihood analysis.
Of all patients, 17 developed RILD in which 8 were of Child-Pugh Grade A, and 9 were of Child-Pugh Grade B. The prediction of RILD by the Michigan model was underestimated for PLC patients. The modified n, m, TD50 (1) were 1.1, 0.28, and 40.5 Gy and 0.7, 0.43, and 23 Gy for patients with Child-Pugh A and B, respectively, which yielded better estimations of RILD probability. The hepatic tolerable doses (TD5) would be MDTNL of 21 Gy and 6 Gy, respectively, for Child-Pugh A and B patients.
The Michigan model was probably not fit to predict RILD in PLC patients. A modified Lyman NTCP model for RILD was recommended.
应用莱曼-库彻-伯曼正常组织并发症(NTCP)模型描述超分割三维适形放疗(3D-CRT)治疗原发性肝癌(PLC)时放射性肝损伤(RILD)的发生概率。
对109例接受3D-CRT治疗的PLC患者进行RILD随访。其中,93例为Child-Pugh A级肝硬化患者,16例为Child-Pugh B级患者。采用密歇根NTCP模型预测RILD的发生概率,然后通过最大似然分析为Child-Pugh A级和Child-Pugh B级患者生成改良的莱曼NTCP模型。
所有患者中,17例发生RILD,其中8例为Child-Pugh A级,9例为Child-Pugh B级。密歇根模型对PLC患者RILD的预测值偏低。Child-Pugh A级和B级患者改良后的n、m、TD50(1)分别为1.1、0.28、40.5 Gy和0.7、0.43、23 Gy,对RILD发生概率的估计效果更好。Child-Pugh A级和B级患者的肝脏耐受剂量(TD5)分别为21 Gy和6 Gy的平均肝门剂量(MDTNL)。
密歇根模型可能不适用于预测PLC患者的RILD。推荐使用改良的莱曼NTCP模型预测RILD。