Tucker Susan L, Liu H Helen, Liao Zhongxing, Wei Xiong, Wang Shulian, Jin Hekun, Komaki Ritsuko, Martel Mary K, Mohan Radhe
Department of Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030, USA.
Int J Radiat Oncol Biol Phys. 2008 Oct 1;72(2):568-74. doi: 10.1016/j.ijrobp.2008.04.053.
To introduce a version of the Lyman normal-tissue complication probability (NTCP) model adapted to incorporate censored time-to-toxicity data and clinical risk factors and to apply the generalized model to analysis of radiation pneumonitis (RP) risk.
Medical records and radiation treatment plans were reviewed retrospectively for 576 patients with non-small cell lung cancer treated with radiotherapy. The time to severe (Grade >/=3) RP was computed, with event times censored at last follow-up for patients not experiencing this endpoint. The censored time-to-toxicity data were analyzed using the standard and generalized Lyman models with patient smoking status taken into account.
The generalized Lyman model with patient smoking status taken into account produced NTCP estimates up to 27 percentage points different from the model based on dose-volume factors alone. The generalized model also predicted that 8% of the expected cases of severe RP were unobserved because of censoring. The estimated volume parameter for lung was not significantly different from n = 1, corresponding to mean lung dose.
NTCP models historically have been based solely on dose-volume effects and binary (yes/no) toxicity data. Our results demonstrate that inclusion of nondosimetric risk factors and censored time-to-event data can markedly affect outcome predictions made using NTCP models.
介绍一种经调整的莱曼正常组织并发症概率(NTCP)模型版本,该模型纳入了删失的毒性发生时间数据和临床风险因素,并将该广义模型应用于放射性肺炎(RP)风险分析。
对576例接受放射治疗的非小细胞肺癌患者的病历和放射治疗计划进行回顾性审查。计算出现严重(≥3级)RP的时间,对于未出现该终点的患者,事件时间在最后一次随访时删失。使用标准和广义莱曼模型分析删失的毒性发生时间数据,并考虑患者的吸烟状态。
考虑患者吸烟状态的广义莱曼模型得出的NTCP估计值与仅基于剂量体积因素的模型相比,相差高达27个百分点。广义模型还预测,由于删失,8%的预期严重RP病例未被观察到。肺的估计体积参数与n = 1(对应平均肺剂量)无显著差异。
历史上NTCP模型仅基于剂量体积效应和二元(是/否)毒性数据。我们的结果表明,纳入非剂量学风险因素和删失的事件发生时间数据会显著影响使用NTCP模型做出的结果预测。