Quantitative CT Evaluation of Small Pulmonary Vessels Has Functional and Prognostic Value in Pulmonary Hypertension

Precision Medicine for Pulmonary Vascular Disease: The Future Is Now (2023 Grover Conference Series)

Pulmonary vascular disease is not a single condition; rather it can accompany a variety of pathologies that impact the pulmonary vasculature. Applying precision medicine strategies to better phenotype, diagnose, monitor, and treat pulmonary vascular disease is increasingly possible with the growing accessibility of powerful clinical and research tools. Nevertheless, challenges exist in implementing these tools to optimal effect. The 2023 Grover Conference Series reviewed the research landscape to summarize the current state of the art and provide a better understanding of the application of precision medicine to managing pulmonary vascular disease. In particular, the following aspects were discussed: (1) Clinical phenotypes, (2) genetics, (3) epigenetics, (4) biomarker discovery, (5) application of precision biology to clinical trials, (6) the right ventricle (RV), and (7) integrating precision medicine to clinical care. The present review summarizes the content of these discussions and the prospects for the future.

Automated volumetry of core and peel intrapulmonary vasculature on computed tomography angiography for non-invasive estimation of hemodynamics in patients with pulmonary hypertension (2022 updated hemodynamic definition)

Background

Computed tomography pulmonary angiography (CTPA) is frequently performed in patients with pulmonary hypertension (PH) and may aid non-invasive estimation of pulmonary hemodynamics. We, therefore, investigated automated volumetry of intrapulmonary vasculature on CTPA, separated into core and peel fractions of the lung volume and its potential to differentially reflect pulmonary hemodynamics in patients with pre- and postcapillary PH.

Methods

A retrospective case-control study of 72 consecutive patients with PH according to the 2022 joint guidelines of the European Society of Cardiology and the European Respiratory Society who underwent right heart catheterization (RHC) and CTPA within 7 days between August 2013 and February 2016 at Thoraxklinik at Heidelberg University Hospital (Heidelberg, Germany) was conducted. Vessel segmentation was performed using the in-house software YACTA. Vascular volumes in different core and peel fractions of the lung were corrected for body surface area. Spearman correlation coefficients with mean pulmonary arterial pressure (mPAP), pulmonary arterial wedge pressure (PAWP) and pulmonary vascular resistance (PVR) were calculated, and a linear regression analysis was done to account for potential confounders.

Results

Median age of the study sample was 71.5 years [interquartile range (IQR), 60.0-77.0 years], 48 (66.67%) were female. Median mPAP was 35.5 mmHg (IQR, 27.0-47.2 mmHg). Postcapillary PH was present in 24/72 (33.3%) patients and precapillary PH in 48/72 (66.7%) patients. Moderate to strong correlations between core intrapulmonary vessel volumes and mPAP were observed in postcapillary PH patients with a maximum at 50% core lung volume (r=0.71, P<0.001). No significant influence of age or sex on this relationship was identified. Correlation with RHC measurements was weak or negligible in patients with precapillary PH.

Conclusions

Automated volumetry of vessels in the core lung strongly correlated with mPAP in patients with postcapillary PH and has potential for non-invasive assessment of postcapillary PH in patients undergoing CTPA.

Chronic diesel exhaust exposure induced pulmonary vascular remodeling a potential trajectory for traffic related pulmonary hypertension

BACKGROUND

As one of the most common traffic-related pollutants, diesel exhaust (DE) confers high risk for cardiovascular and respiratory diseases. However, its impact on pulmonary vessels is still unclear.

METHODS

To explore the effects of DE exposure on pulmonary vascular remodeling, our study analyzed the number and volume of small pulmonary vessels in the diesel engine testers (the DET group) from Luoyang Diesel Engine Factory and the controls (the non-DET group) from the local water company, using spirometry and carbon content in airway macrophage (CCAM) in sputum. And then we constructed a rat model of chronic DE exposure, in which 12 rats were divided into the DE group (6 rats with 16-week DE exposure) and the control group (6 rats with 16-week clean air exposure). During right heart catheterization, right ventricular systolic pressure (RVSP) was assessed by manometry. Macrophage migration inhibitory factor (MIF) in lung tissues and bronchoalveolar lavage fluid (BALF) were measured by qRT-PCR and ELISA, respectively. Histopathological analysis for cardiovascular remodeling was also performed.

RESULTS

In DET cohort, the number and volume of small pulmonary vessels in CT were positively correlated with CCAM in sputum (P<0.05). Rat model revealed that chronic DE-exposed rats had elevated RVSP, along with increased wall thickness of pulmonary small vessels and right the ventricle. What's more, the MIF levels in BALF and lung tissues were higher in DE-exposed rats than the controls.

CONCLUSION

Apart from airway remodeling, DE also induces pulmonary vascular remodeling, which will lead to cardiopulmonary dysfunction.

Non-contrasted computed tomography (NCCT) based chronic thromboembolic pulmonary hypertension (CTEPH) automatic diagnosis using cascaded network with multiple instance learning

Objective.The diagnosis of chronic thromboembolic pulmonary hypertension (CTEPH) is challenging due to nonspecific early symptoms, complex diagnostic processes, and small lesion sizes. This study aims to develop an automatic diagnosis method for CTEPH using non-contrasted computed tomography (NCCT) scans, enabling automated diagnosis without precise lesion annotation.Approach.A novel cascade network (CN) with multiple instance learning (CNMIL) framework was developed to improve the diagnosis of CTEPH. This method uses a CN architecture combining two Resnet-18 CNN networks to progressively distinguish between normal and CTEPH cases. Multiple instance learning (MIL) is employed to treat each 3D CT case as a 'bag' of image slices, using attention scoring to identify the most important slices. An attention module helps the model focus on diagnostically relevant regions within each slice. The dataset comprised NCCT scans from 300 subjects, including 117 males and 183 females, with an average age of 52.5 ± 20.9 years, consisting of 132 normal cases and 168 cases of lung diseases, including 88 cases of CTEPH. The CNMIL framework was evaluated using sensitivity, specificity, and the area under the curve (AUC) metrics, and compared with common 3D supervised classification networks and existing CTEPH automatic diagnosis networks.Main results. The CNMIL framework demonstrated high diagnostic performance, achieving an AUC of 0.807, accuracy of 0.833, sensitivity of 0.795, and specificity of 0.849 in distinguishing CTEPH cases. Ablation studies revealed that integrating MIL and the CN significantly enhanced performance, with the model achieving an AUC of 0.978 and perfect sensitivity (1.000) in normal classification. Comparisons with other 3D network architectures confirmed that the integrated model outperformed others, achieving the highest AUC of 0.8419.Significance. The CNMIL network requires no additional scans or annotations, relying solely on NCCT. This approach can improve timely and accurate CTEPH detection, resulting in better patient outcomes.

CT-derived lung vessel morphology correlates with prognostic markers in precapillary pulmonary hypertension

BACKGROUND

While computed tomography pulmonary angiography (CTPA) is an integral part of the work-up in patients with suspected pulmonary hypertension (PH), there is no established CTPA-derived prognostic marker. We aimed to assess whether quantitative readouts of lung vessel morphology correlate with established prognostic indicators in PH.

METHODS

We applied a fully-automatic in-house developed algorithm for segmentation of arteries and veins to determine lung vessel morphology in patients with precapillary PH who underwent right heart catheterization and CTPA between May 2016 and May 2019. Primary endpoint of this retrospective study was the calculation of receiver operating characteristics for identifying low and high mortality risk according to the 3-strata risk assessment model presented in the current guidelines.

RESULTS

We analyzed 73 patients, median age 65 years (interquartile range (IQR): 54-76), female/male ratio 35/38, median mean pulmonary arterial pressure 37 mm Hg (IQR: 30-46), and found significant correlations with important prognostic factors in pulmonary arterial hypertension. N-terminal pro-brain natriuretic peptide, cardiac index, mixed venous oxygen saturation, and 6-minute walking distance were correlated with the ratio of the number of arteries over veins with vessel diameters of 6-10 mm (Spearman correlation coefficients ρ = 0.64, p < 0.001; ρ = -0.60, p < 0.001; ρ = -0.47, p = 0.005; ρ = -0.45, p = 0.001, respectively). This ratio predicted a low- and high-risk score with an area under the curve of 0.73 (95% confidence interval (CI): 0.56-0.90) and 0.86 (95% CI: 0.74-0.97), respectively.

CONCLUSIONS

The ratio of the number of arteries over veins with diameters between 6 and 10 mm is significantly correlated with prognostic markers in pulmonary hypertension and predicts low and high mortality risk.

Diagnostic value of 3D volume measurement of central pulmonary artery based on CTPA images in the pulmonary hypertension

BACKGROUND

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).

METHODS

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.

RESULTS

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.

CONCLUSION

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.

DRU-Net: Pulmonary Artery Segmentation via Dense Residual U-Network with Hybrid Loss Function

The structure and topology of the pulmonary arteries is crucial to understand, plan, and conduct medical treatment in the thorax area. Due to the complex anatomy of the pulmonary vessels, it is not easy to distinguish between the arteries and veins. The pulmonary arteries have a complex structure with an irregular shape and adjacent tissues, which makes automatic segmentation a challenging task. A deep neural network is required to segment the topological structure of the pulmonary artery. Therefore, in this study, a Dense Residual U-Net with a hybrid loss function is proposed. The network is trained on augmented Computed Tomography volumes to improve the performance of the network and prevent overfitting. Moreover, the hybrid loss function is implemented to improve the performance of the network. The results show an improvement in the Dice and HD95 scores over state-of-the-art techniques. The average scores achieved for the Dice and HD95 scores are 0.8775 and 4.2624 mm, respectively. The proposed method will support physicians in the challenging task of preoperative planning of thoracic surgery, where the correct assessment of the arteries is crucial.