Machine learning assisted feature identification and prediction of hemodynamic endpoints using computed tomography in patients with CTEPH

Abstract

Chronic thromboembolic pulmonary hypertension (CTEPH) is a rare but potentially curable cause of pulmonary hypertension (PH). Currently PH is diagnosed by right heart catheterisation. Computed tomography (CT) is used for ruling out other causes and operative planning. This study aims to evaluate importance of different quantitative/qualitative imaging features and develop a supervised machine learning (ML) model to predict hemodynamic risk groups. 127 Patients with diagnosed CTEPH who received preoperative right heart catheterization and thoracic CTA examinations (39 ECG-gated; 88 non-ECG gated) were included. 19 qualitative/quantitative imaging features and 3 hemodynamic parameters [mean pulmonary artery pressure, right atrial pressure (RAP), pulmonary artery oxygen saturation (PA SaO2)] were gathered. Diameter-based CT features were measured in axial and adjusted multiplane reconstructions (MPR). Univariate analysis was performed for qualitative and quantitative features. A random forest algorithm was trained on imaging features to predict hemodynamic risk groups. Feature importance was calculated for all models. Qualitative and quantitative parameters showed no significant differences between ECG and non-ECG gated CTs. Depending on reconstruction plane, five quantitative features were significantly different, but mean absolute difference between parameters (MPR vs. axial) was 0.3 mm with no difference in correlation with hemodynamic parameters. Univariate analysis showed moderate to strong correlation for multiple imaging features with hemodynamic parameters. The model achieved an AUC score of 0.82 for the mPAP based risk stratification and 0.74 for the PA SaO2 risk stratification. Contrast agent retention in hepatic vein, mosaic attenuation pattern and the ratio right atrium/left ventricle were the most important features among other parameters. Quantitative and qualitative imaging features of reconstructions correlate with hemodynamic parameters in preoperative CTEPH patients-regardless of MPR adaption. Machine learning based analysis of preoperative imaging features can be used for non-invasive risk stratification. Qualitative features seem to be more important than previously anticipated.

Keywords: Artificial intelligence; CTEPH; Computed tomography; Pulmonary hypertension.

Fig. 1

Measurements of computed tomography parameters in axial plane and multiplanar reconstruction. Shown is the different measurement technique in simple axial (left) and multiplanar reconstruction (right) for the same parameter—pulmonary trunc diameter. Difference in pulmonary trunc diameter was 0.1 mm in this patient. Further, the exemplary measurement of the left atrium in MPR is show

Fig. 2

Exemplary images of qualitative findings. Shown are exemplary images of the different qualitative findings (from upper left to lower right): pericardial effusion, mosaic perfusion, groundglass opacities, contrast media retention in hepatic veins

Fig. 3

Coefficients of determination for all quantitative features with respect to the hemodynamic parameters. Measurement ratios show moderate to strong correlation with all three hemodynamic parameters. Shown are the correlations strengths of the quantitative computed tomography parameters and the hemodynamic parameters from right heart catheterization. Strongest correlations were found for: pulmonary trunc pressure—left pulmonary artery diameter (r² = 0.49), right atrial pressure—short axis right atrium (r² = 0.5) and pulmonary trunc SaO2—ratio right atrial diameter/left ventricle diameter (r² = − 0.51)

Fig. 4

Boxplots in regard to qualitative features. Shown are the differences in qualitative features regarding right atrial pressure, pulmonary trunc pressure and pulmonary trunc partial oxygen pressure. Highly significant differences are marked with *** and significant differences with *