The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China; Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin.
Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin.
Int J Radiat Oncol Biol Phys. 2018 Nov 15;102(4):1308-1318. doi: 10.1016/j.ijrobp.2018.04.059. Epub 2018 May 1.
To investigate radiation-induced changes of computed tomography (CT) textures in parotid glands (PG) to predict acute xerostomia during radiotherapy (RT) for head and neck cancer (HNC).
Daily or fraction kilovoltage CTs acquired using diagnostic CT scanners (eg, in-room CTs) during intensity-modulated RT for 59 HNC patients at 3 institutions were analyzed. The PG contours were generated on selected daily/fraction CTs. A series of histogram-based texture features, including the mean CT number (MCTN) in Hounsfield units, volume, standard deviation, skewness, kurtosis, and entropy for PGs were calculated for each fraction. Correlations between the changes of the texture features, radiation dose, and observed acute xerostomia were analyzed. A classifier model and the incurred CT-based xerostomia score (CTXS) were introduced to predict xerostomia based on combined changes of MCTN and volume of PGs. The t test and Spearman and Pearson correlation tests were used in the analyses.
Substantial changes in various CT texture features of PGs were observed during RT delivery. The changes of PG MCTN or volume are not strongly correlated with the observed xerostomia grades if they are considered separately. The CTXS showed a significant correlation to the observed xerostomia grades (r = 0.71, P < .00001). The CTXS-based classifier can predict the xerostomia severity with a success rate ranging from 79% to 98%. The xerostomia severity at the end of treatment can be predicted based on the CTXS determined at the fifth week with a precision and sensitivity of 100%.
Significant changes in the CT histogram features of the parotid glands were observed during RT of HNC. A practical method of using the changes of MCTN and volume of PGs is proposed to predict radiation-induced acute xerostomia, which may be used to help design adaptive treatment.
研究腮腺（PG）在接受头颈部癌症（HNC）调强放疗（RT）过程中的 CT 纹理变化，以预测急性口干症。
在 3 家机构对 59 例 HNC 患者进行调强 RT 治疗时，每天或分次千伏 CT 由诊断 CT 扫描仪（如机房 CT）采集。在选定的每日/分次 CT 上生成 PG 轮廓。计算每个分次的 PG 一系列基于直方图的纹理特征，包括 CT 号均值（MCTN）、体积、标准差、偏度、峰度和熵。分析纹理特征变化、辐射剂量与观察到的急性口干症之间的相关性。引入分类器模型和基于 CT 的口干症评分（CTXS），根据 PG 的 MCTN 和体积的综合变化预测口干症。分析中使用了 t 检验、斯皮尔曼和皮尔逊相关检验。
在 RT 治疗过程中，观察到 PG 的各种 CT 纹理特征发生了明显变化。如果单独考虑，PG 的 MCTN 或体积变化与观察到的口干症分级没有很强的相关性。CTXS 与观察到的口干症分级显著相关（r=0.71，P<0.00001）。基于 CTXS 的分类器可以预测口干症严重程度，成功率在 79%到 98%之间。根据第 5 周确定的 CTXS，可以预测治疗结束时的口干症严重程度，精度和灵敏度为 100%。
在 HNC 的 RT 过程中，观察到腮腺 CT 直方图特征发生了显著变化。提出了一种使用 PG 的 MCTN 和体积变化来预测放射性急性口干症的实用方法，该方法可能有助于设计适应性治疗。
