Establishing a 4D-CT lung function related volumetric dose model to reduce radiation pneumonia

• Published in: Scientific reports Vol. 14; no. 1; pp. 12589 - 12
• Main Authors: Liu, Chunmei, Liu, Huizhi, Li, Yange, Xiao, Zhiqing, Wang, Yanqiang, Guo, Han, Luo, Jianmin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.06.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 4D-CT; 692/308; 692/4028; 692/499; Adult; Aged; Calibration; Computed tomography; Female; Four-Dimensional Computed Tomography - methods; Functional imaging; Humanities and Social Sciences; Humans; Localization; Lung - diagnostic imaging; Lung - radiation effects; Lung cancer; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - radiotherapy; Lungs; Male; Middle Aged; multidisciplinary; Pneumonia; Prediction models; Radiation pneumonia; Radiation Pneumonitis - diagnostic imaging; Radiation therapy; Radiotherapy Dosage; Radiotherapy Planning, Computer-Assisted - methods; Respiratory function; Science; Science (multidisciplinary); Ventilation; 4D-CT; Radiation pneumonia; Lung cancer; Functional imaging
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-63251-0

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.