Medical Physics Department, San Raffaele Scientific Institute, Milano, Italy.
Radiother Oncol. 2010 Feb;94(2):206-12. doi: 10.1016/j.radonc.2009.12.014. Epub 2010 Feb 1.
To assess anatomical, clinical and dosimetric pre-treatment parameters, possibly predictors of parotid shrinkage during radiotherapy of head and neck cancer (HNC).
Data of 174 parotids from four institutions were analysed; patients were treated with IMRT, with radical and adjuvant intent. Parotid shrinkage was evaluated by the volumetric difference (DeltaV) between parotid volumes at the end and those at the start of the therapy, as assessed by CT images (MVCT for 40 patients, KVCT for 47 patients). Correlation between DeltaVcc/% and a number of dosimetric, clinical and geometrical parameters was assessed. Univariate as well as stepwise logistic multivariate (MVA) analyses were performed by considering as an end-point a DeltaVcc/% larger than the median value. Linear models of DeltaV (continuous variable) based on the most predictive variables found at the MVA were developed.
Median DeltaVcc/% were 6.95 cc and 26%, respectively. The most predictive independent variables of DeltaVcc at MVA were the initial parotid volume (IPV, OR: 1.100; p=0.0002) and Dmean (OR: 1.059; p=0.038). The main independent predictors of DeltaV% at MVA were age (OR: 0.968; p=0.041) and V40 (OR: 1.0338; p=0.013). DeltaVcc and DeltaV% may be well described by the equations: DeltaVcc=-2.44+0.076 Dmean (Gy)+0.279 IPV (cc) and DeltaV%=34.23+0.192 V40 (%)-0.2203 age (year). The predictive power of the DeltaVcc model is higher than that of the DeltaV% model.
IPV/age and Dmean/V40 are the major dosimetric and clinical/anatomic predictors of DeltaVcc and DeltaV%. DeltaVcc and DeltaV% may be well described by bi-linear models including the above-mentioned variables.
评估头颈部癌症（HNC）放疗中腮腺退缩的解剖学、临床和剂量学的预处理参数，这些参数可能是预测因素。
对四个机构的 174 个腮腺的数据进行了分析；患者接受了调强放疗（IMRT），包括根治性和辅助性治疗。通过 CT 图像（40 例患者为 MVCT，47 例患者为 KVCT）评估治疗前后腮腺体积的体积差（DeltaV）来评估腮腺退缩。评估了 DeltaVcc/%与多种剂量学、临床和几何参数之间的相关性。通过考虑终点为 DeltaVcc/%大于中位数的情况，进行了单变量和逐步逻辑多元分析（MVA）。根据 MVA 中发现的最具预测性的变量，建立了基于 DeltaV（连续变量）的线性模型。
DeltaVcc/%的中位数分别为 6.95 cc 和 26%。MVA 中 DeltaVcc 的最具预测性的独立变量是初始腮腺体积（IPV，OR：1.100；p=0.0002）和 Dmean（OR：1.059；p=0.038）。MVA 中 DeltaV%的主要独立预测因子是年龄（OR：0.968；p=0.041）和 V40（OR：1.0338；p=0.013）。DeltaVcc 和 DeltaV%可以通过以下方程很好地描述：DeltaVcc=-2.44+0.076 Dmean（Gy）+0.279 IPV（cc）和 DeltaV%=34.23+0.192 V40（%）-0.2203 age（年）。DeltaVcc 模型的预测能力高于 DeltaV%模型。
IPV/年龄和 Dmean/V40 是 DeltaVcc 和 DeltaV%的主要剂量学和临床/解剖学预测因子。DeltaVcc 和 DeltaV%可以通过包含上述变量的双线性模型很好地描述。
