Chen Qiongzhu, Ouyang Lechuan, Li Qianyi, Xia Ziyang, Li Xian, Liu Chunli, Kim Seong-Hyop, Brunelli Alessandro, Lan Rihui, Song Yuquan
Department of Radiology, National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
State Key Laboratory of Respiratory Diseases, Guangzhou Institute of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University China, Guangzhou, China.
Quant Imaging Med Surg. 2024 Feb 1;14(2):1564-1576. doi: 10.21037/qims-23-1186. Epub 2024 Jan 22.
Chest dynamic digital radiography (DDR) is used as a supplementary tool for the routine pulmonary function test (PFT); however, its potential as a novel standard PFT method has yet to be explored. Therefore, the present study aimed to investigate the correlation between the change in the projected lung area (ΔPLA) and forced vital capacity (FVC) using chest DDR, and to establish a DDR-FVC estimation model and a predictive value model for the ΔPLA.
In total, 139 participants who underwent chest DDR and the PFT in the same period at The First Affiliated Hospital of Guangzhou Medical University from April 2022 to February 2023 were prospectively included in the study. The patients' age, gender, height, and weight measurements were recorded. Additionally, the ΔPLA was measured, and the IWS workstation software was used for automated outlining and calculation. Subsequently, a correlation analysis and regression analysis models were employed to examine the relationship between the ΔPLA, FVC, and individual physiological characteristics. Additionally, an independent sample -test was used to determine whether there were any significant differences between the normal and abnormal FVC groups.
The 139 participants were grouped according to the results of the ratio of measured/predicted FVC values (FVC%pred); those with an FVC%pred ≥80%, were allocated to the normal FVC group, and those with an FVC%pred <80% were allocated to the abnormal FVC group. The correlation coefficient was >0.8 in the full sample; the ΔPLA showed a significant linear correlation with the measured FVC value [r=0.81, 95% confidence interval (CI): 0.75-0.86, P<0.001]. There was a significant difference in the ΔPLA between the normal and abnormal FVC groups. With the ΔPLA, age, gender, height, and weight as predictor variables, the following DDR-FVC estimation model was established: DDR-FVC estimation model = -0.997 + 1.35×10 × ΔPLA + 0.017 × height - 0.014 × age + 0.249 × gender (1 for male and 0 for female) [adjusted R (adj. R)=0.731, =94.615, P<0.001]. The following formula was used to determine the predictive value of the ΔPLA: Predictive value of ΔPLA = -12,504.287 + 173.185 × height + 62.971 × weight - 84.933 × age (adj. R=0.393, =20.453, P<0.001).
There was a linear correlation between the ΔPLA measured by biphasic chest DDR and the FVC. A model for estimating the FVC was established based on the ΔPLA, which allows the FVC to be assessed by the ΔPLA measured by biphasic chest DDR. A predictive value model for the ΔPLA was also established to provide ΔPLA reference values for assessment and comparison.
胸部动态数字放射成像（DDR）被用作常规肺功能测试（PFT）的辅助工具；然而，其作为一种新型标准PFT方法的潜力尚未得到探索。因此，本研究旨在利用胸部DDR研究预计肺面积变化（ΔPLA）与用力肺活量（FVC）之间的相关性，并建立DDR-FVC估计模型和ΔPLA的预测值模型。
2022年4月至2023年2月期间在广州医科大学附属第一医院同期接受胸部DDR和PFT的139名参与者被前瞻性纳入本研究。记录患者的年龄、性别、身高和体重测量值。此外，测量ΔPLA，并使用IWS工作站软件进行自动轮廓勾勒和计算。随后，采用相关性分析和回归分析模型来研究ΔPLA、FVC和个体生理特征之间的关系。此外，使用独立样本t检验来确定正常FVC组和异常FVC组之间是否存在显著差异。
根据测量/预测FVC值的比值（FVC%pred）结果将139名参与者分组；FVC%pred≥80%的参与者被分配到正常FVC组，FVC%pred<80%的参与者被分配到异常FVC组。全样本中的相关系数>0.8；ΔPLA与测量的FVC值呈显著线性相关[r = 0.81，95%置信区间（CI）：0.75 - 0.86，P < 0.001]。正常FVC组和异常FVC组之间的ΔPLA存在显著差异。以ΔPLA、年龄、性别、身高和体重作为预测变量，建立了以下DDR-FVC估计模型：DDR-FVC估计模型 = -0.997 + 1.35×10×ΔPLA + 0.017×身高 - 0.014×年龄 + 0.249×性别（男性为1，女性为0）[调整后R（adj. R） = 0.731，F = 94.615，P < 0.001]。使用以下公式确定ΔPLA的预测值：ΔPLA的预测值 = -12,504.287 + 173.185×身高 + 62.971×体重 - 84.933×年龄（adj. R = 0.393，F = 20.453，P < 0.001）。
双相胸部DDR测量的ΔPLA与FVC之间存在线性相关性。基于ΔPLA建立了FVC估计模型，该模型允许通过双相胸部DDR测量的ΔPLA来评估FVC。还建立了ΔPLA的预测值模型，以提供用于评估和比较的ΔPLA参考值。
