PDF(Pubmed)
Abstract：
In order to study how to use pulmonary functional imaging obtained through 4D-CT fusion for radiotherapy planning, and transform traditional dose volume parameters into functional dose volume parameters, a functional dose volume parameter model that may reduce level 2 and above radiation pneumonia was obtained. 41 pulmonary tumor patients who underwent 4D-CT in our department from 2020 to 2023 were included. MIM Software (MIM 7.0.7; MIM Software Inc., Cleveland, OH, USA) was used to register adjacent phase CT images in the 4D-CT series. The three-dimensional displacement vector of CT pixels was obtained when changing from one respiratory state to another respiratory state, and this three-dimensional vector was quantitatively analyzed. Thus, a color schematic diagram reflecting the degree of changes in lung CT pixels during the breathing process, namely the distribution of ventilation function strength, is obtained. Finally, this diagram is fused with the localization CT image. Select areas with Jacobi > 1.2 as high lung function areas and outline them as fLung. Import the patient\'s DVH image again, fuse the lung ventilation image with the localization CT image, and obtain the volume of fLung different doses (V60, V55, V50, V45, V40, V35, V30, V25, V20, V15, V10, V5). Analyze the functional dose volume parameters related to the risk of level 2 and above radiation pneumonia using R language and create a predictive model. By using stepwise regression and optimal subset method to screen for independent variables V35, V30, V25, V20, V15, and V10, the prediction formula was obtained as follows: Risk = 0.23656-0.13784 * V35 + 0.37445 * V30-0.38317 * V25 + 0.21341 * V20-0.10209 * V15 + 0.03815 * V10. These six independent variables were analyzed using a column chart, and a calibration curve was drawn using the calibrate function. It was found that the Bias corrected line and the Apparent line were very close to the Ideal line, The consistency between the predicted value and the actual value is very good. By using the ROC function to plot the ROC curve and calculating the area under the curve: 0.8475, 95% CI 0.7237-0.9713, it can also be determined that the accuracy of the model is very high. In addition, we also used Lasso method and random forest method to filter out independent variables with different results, but the calibration curve drawn by the calibration function confirmed poor prediction performance. The function dose volume parameters V35, V30, V25, V20, V15, and V10 obtained through 4D-CT are key factors affecting radiation pneumonia. Establishing a predictive model can provide more accurate lung restriction basis for clinical radiotherapy planning.
摘要：
为了研究如何将通过4D-CT融合获得的肺功能成像用于放射治疗计划,并将传统剂量体积参数转化为功能剂量体积参数，获得了可能降低2级及以上放射性肺炎的功能剂量体积参数模型。纳入了2020年至2023年在我科接受4D-CT检查的41例肺肿瘤患者。MIM软件（MIM7.0.7；MIM软件公司，克利夫兰,OH,USA）用于配准4D-CT系列中的相邻相位CT图像。获得了从一种呼吸状态到另一种呼吸状态变化时CT像素的三维位移矢量,并对这个三维矢量进行了定量分析。因此，反映呼吸过程中肺部CT像素变化程度的彩色示意图，即通风功能强度的分布，已获得。最后,该图与定位CT图像融合。选择Jacobi>1.2的区域作为高肺功能区域，并将其勾勒为fLung。再次导入患者的DVH图像，将肺通气图像与定位CT图像融合，并获得不同剂量(V60、V55、V50、V45、V40、V35、V30、V25、V20、V15、V10、V5)的体积。利用R语言分析与2级及以上放射性肺炎风险相关的功能剂量体积参数,并建立预测模型。通过逐步回归和最优子集法筛选自变量V35、V30、V25、V20、V15和V10，得到预测公式为：Risk=0.23656-0.13784*V35+0.37445*V30-0.38317*V25+0.21341*V20-0.10*V15+0.038209*V10。这六个独立变量用柱状图分析，并使用校准函数绘制校准曲线。发现偏差校正线和表观线非常接近理想线，预测值与实际值的一致性非常好。通过使用ROC函数绘制ROC曲线并计算曲线下面积：0.8475，95％CI0.7237-0.9713，也可以确定模型的准确性很高。此外,我们还使用Lasso方法和随机森林方法筛选出结果不同的独立变量，但是校准函数绘制的校准曲线证实了较差的预测性能。通过4D-CT获得的功能剂量体积参数V35、V30、V25、V20、V15和V10是影响放射性肺炎的关键因素。建立预测模型可以为临床放疗计划提供更准确的肺限制依据。
