背景:这项回顾性研究旨在评估使用计算机断层扫描肺动脉造影(CTPA)对中央肺动脉进行体积测量的诊断价值,以预测肺动脉高压(PH)。
方法:选择2013年11月至2021年4月在我院接受右心导管(RHC)和CTPA检查的59例患者。肺动脉收缩压(SPAP),平均PAP(mPAP),和舒张PAP(DPAP)从RHC测试中获得。将患者分为非PH组(18例)和PH组(41例)。主肺动脉(DMPA)的直径,右肺动脉(DRPA),手动测量左肺动脉(DLPA)。3D模型软件用于中央肺动脉的分割。横截面积(AMPA,ARPA,ALPA)和卷(VMPA,VRPA,计算VLPA)。比较两组CTPA图像对肺动脉的测量结果,并与RHC检测参数相关。使用ROC曲线和决策曲线分析(DCA)评估三维CTPA参数预测PH的益处。采用具有正向步法的多元线性回归模型来整合所有具有统计学意义的CTPA参数以进行PH预测。
结果:所有参数(DMPA,DRPA,DLPA,AMPA,ARPA,ALPA,VMPA,VRPA,与非PH组相比,PH组CTPA和VLPA)图像显着升高(P<0.05),并与RHC测试参数呈正相关(mPAP,DPAP,SPAP)(对于MPA,r范围为0.586~0.752,RPA为0.527~0.640,LPA为0.302~0.495,均P<0.05)。对于MPA和RPA,与一维和二维参数相比,3D参数显示出更高的相关系数。ROC分析表明,VMPA显示出比DMPA和AMPA更高的曲线下面积(AUC),VRPA的AUC明显高于DRPA和ARPA(DRPA与VRPA,Z=2.029,P=0.042;ARPA与VRPA,Z=2.119,P=0.034)。DCA表明,三维参数可以为MPA和RPA提供很大的净收益。mPAP的预测方程,DPAP,和SPAP被制定为[8.178+0.0006*VMPA],[1.418+0.0005*VMPA],和[-11.137+0.0006*VRPA+1.259*DMPA],分别。
结论:基于CTPA图像的MPA和RPA的3D体积测量在预测PH方面可能比传统的直径和横截面积更多。
This retrospective study aims to evaluate the diagnostic value of volume measurement of central pulmonary arteries using computer tomography pulmonary angiography (CTPA) for predicting pulmonary hypertension (PH).
A total of 59 patients in our hospital from November 2013 to April 2021 who underwent both right cardiac catheterization (RHC) and CTPA examination were included. Systolic pulmonary artery pressure (SPAP), mean PAP (mPAP), and diastolic PAP (DPAP) were acquired from RHC testing. Patients were divided into the non-PH group (18 cases) and the PH group (41 cases). The diameters of the main pulmonary artery (DMPA), right pulmonary artery (DRPA), and left pulmonary artery (DLPA) were measured manually. A 3D model software was used for the segmentation of central pulmonary arteries. The cross-sectional areas (AMPA, ARPA, ALPA) and the volumes (VMPA, VRPA, VLPA) were calculated. Measurements of the pulmonary arteries derived from CTPA images were compared between the two groups, and correlated with the parameters of RHC testing. ROC curves and decision curve analysis (DCA) were used to evaluate the benefit of the three-dimensional CTPA parameters for predicting PH. A multiple linear regression model with a forward-step approach was adopted to integrate all statistically significant CTPA parameters for PH prediction.
All parameters (DMPA, DRPA, DLPA, AMPA, ARPA, ALPA, VMPA, VRPA, and VLPA) of CTPA images exhibited significantly elevated in the PH group in contrast to the non-PH group (P < 0.05), and showed positive correlations with the parameters of RHC testing (mPAP, DPAP, SPAP) (r ranged 0.586~0.752 for MPA, 0.527~0.640 for RPA, and 0.302~0.495 for LPA, all with P < 0.05). For the MPA and RPA, 3D parameters showed higher correlation coefficients compared to their one-dimensional and two-dimensional counterparts. The ROC analysis indicated that the VMPA showed higher area under the curves (AUC) than the DMPA and AMPA without significance, and the VRPA showed higher AUC than the DRPA and ARPA significantly (DRPA vs. VRPA, Z = 2.029, P = 0.042; ARPA vs. VRPA, Z = 2.119, P = 0.034). The DCA demonstrated that the three-dimensional parameters could provide great net benefit for MPA and RPA. The predictive equations for mPAP, DPAP, and SPAP were formulated as [8.178 + 0.0006 * VMPA], [1.418 + 0.0005 * VMPA], and [-11.137 + 0.0006*VRPA + 1.259 * DMPA], respectively.
The 3D volume measurement of the MPA and RPA based on CTPA images maybe more informative than the traditional diameter and cross-sectional area in predicting PH.