Early Changes in Serial CBCT-Measured Parotid Gland Biomarkers Predict Chronic Xerostomia After Head and Neck Radiation Therapy.
Early Changes in Serial CBCT-Measured Parotid Gland Biomarkers Predict Chronic Xerostomia After Head and Neck Radiation Therapy.
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DOI：
10.1016/j.ijrobp.2018.06.048
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发表时间：
2018-11-15
期刊：
影响因子：
--
通讯作者：
El Naqa I
中科院分区：
文献类型：
--
作者：
Rosen BS;Hawkins PG;Polan DF;Balter JM;Brock KK;Kamp JD;Lockhart CM;Eisbruch A;Mierzwa ML;Ten Haken RK;El Naqa I
为了确定在头颈部放疗（HNR）期间拍摄的连续锥形束计算机断层扫描（CBCT）图像是否可以改善慢性口干症预测，在一项回顾性分析中，使用可变形图像配准（ImageRegistration）在119名HNR患者的每日kV CBCT图像上描绘腮腺（PG）（60或70戈伊，2戈伊/次，6或7周）。变形轮廓的子集的测量精度进行了量化使用骰子相似系数（DSC）和平均距离协议（MDA）相比，手动绘制的轮廓。计算每个PG相对于第1周CBCT测量的平均HU强度和体积的平均每周变化。从每个计划中提取剂量体积直方图（DVH）统计数据，并研究剂量、体积和强度之间的相互作用。采用单变量分析和惩罚性logistic回归分析HNR后一年内与非严重性口干的相关性。使用1级或以上（≥1级）和2级或以上（≥2级）口干预测的受试者工作特征曲线下面积（AUC），将包括CBCT delta成像特征的模型与临床/仅DVH模型进行比较。所有患者均发生治疗结束时PG体积缩小，同侧和对侧PG平均（范围）收缩率分别为19.6%（0.9-58.4%）和17.7%（4.4-56.3%）。治疗中期体积变化与平均PG剂量高度相关（r=-0.318，p<1 e-6）。≥1级和≥2级口干的发生率分别为65%和16%。对于≥1级口干症预测，δ成像模型的AUC = 0.719（95% CI：0.603-0.830），而剂量/临床模型的AUC = 0.709（95% CI：0.603-0.815）。对于≥2级口干症预测，剂量/临床模型的AUC = 0.692（95% CI：0.615-0.770），增加对侧PG变化适度改善了预测性能，AUC = 0.776（0.643-0.912）。CBCT测量的PG图像特征变化率改善了单独剂量对慢性口干预测的预测。分析为治疗定位而采集的CBCT图像可以提供一种廉价的监测系统，以支持降低毒性的自适应放射治疗。
To determine whether serial cone-beam computed tomography (CBCT) images taken during head and neck radiotherapy (HNR) can improve chronic xerostomia prediction In a retrospective analysis, parotid glands (PGs) were delineated on daily kV CBCT images using deformable image registration (DIR) for 119 HNR patients (60 or 70 Gy in 2 Gy/fraction over 6 or 7 weeks). DIR accuracy for a subset of deformed contours was quantified using the Dice similarity coefficient (DSC) and mean distance-to-agreement (MDA) in comparison to manually drawn contours. Average weekly changes in CBCT-measured mean HU intensity and volume were calculated for each PG relative to week 1. Dose-volume-histogram (DVH) statistics were extracted from each plan, and interactions between dose, volume, and intensity were investigated. Univariable analysis and penalized logistic regression were used to analyze association with observer-rated xerostomia at one year following HNR. Models including CBCT delta imaging features were compared to clinical/DVH-only models using area under the receiver-operating characteristic curve (AUC) for Grade 1 or higher (Grade≥1) and for Grade 2 or higher (Grade≥2) xerostomia prediction. All patients experienced end-treatment PG volume reduction with mean (range) ipsilateral and contralateral PG shrinkage of 19.6% (0.9-58.4%) and 17.7% (4.4-56.3%), respectively. Mid-treatment volume change was highly correlated with mean PG dose (r=−0.318, p<1e-6). Incidence of Grade≥1 and Grade≥2 xerostomia was 65% and 16%, respectively. For Grade≥1 xerostomia prediction, the delta-imaging model had AUC = 0.719 (95% CI: 0.603-0.830), compared to 0.709 (95% CI: 0.603-0.815) for the dose/clinical model. For Grade≥2 xerostomia prediction, the dose/clinical model had AUC = 0.692 (95% CI: 0.615-0.770), and the addition of contralateral PG changes modestly improved predictive performance with AUC = 0.776 (0.643-0.912). The rate of CBCT-measured PG image feature changes improves prediction for chronic xerostomia prediction over dose alone. Analysis of CBCT images acquired for treatment positioning may provide an inexpensive monitoring system to support toxicity-reducing adaptive radiotherapy.
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