Early Diagnostic Features of Left-to-Right Shunt-Induced Pulmonary Arterial Hypertension in Piglets

QCard-NM: Developing a semiautomatic segmentation method for quantitative analysis of the right ventricle in non-gated myocardial perfusion SPECT imaging

BACKGROUND

Recent studies have shown that the right ventricular (RV) quantitative analysis in myocardial perfusion imaging (MPI) SPECT can be beneficial in the diagnosis of many cardiopulmonary diseases. This study proposes a new algorithm for right ventricular 3D segmentation and quantification.

METHODS

The proposed Quantitative Cardiac analysis in Nuclear Medicine imaging (QCard-NM) algorithm provides RV myocardial surface estimation and creates myocardial contour using an iterative 3D model fitting method. The founded contour is then used for quantitative RV analysis. The proposed method was assessed using various patient datasets and digital phantoms. First, the physician's manually drawn contours were compared to the QCard-NM RV segmentation using the Dice similarity coefficient (DSC). Second, using repeated MPI scans, the QCard-NM's repeatability was evaluated and compared with the QPS (quantitative perfusion SPECT) algorithm. Third, the bias of the calculated RV cavity volume was analyzed using 31 digital phantoms using the QCard-NM and QPS algorithms. Fourth, the ability of QCard-NM analysis to diagnose coronary artery diseases was assessed in 60 patients referred for both MPI and coronary angiography.

RESULTS

The average DSC value was 0.83 in the first dataset. In the second dataset, the coefficient of repeatability of the calculated RV volume between two repeated scans was 13.57 and 43.41 ml for the QCard-NM and QPS, respectively. In the phantom study, the mean absolute percentage errors for the calculated cavity volume were 22.6% and 42.2% for the QCard-NM and QPS, respectively. RV quantitative analysis using QCard-NM in detecting patients with severe left coronary system stenosis and/or three-vessel diseases achieved a fair performance with the area under the ROC curve of 0.77.

CONCLUSION

A novel model-based iterative method for RV segmentation task in non-gated MPI SPECT is proposed. The precision, accuracy, and consistency of the proposed method are demonstrated by various validation techniques. We believe this preliminary study could lead to developing a framework for improving the diagnosis of cardiopulmonary diseases using RV quantitative analysis in MPI SPECT.

Experimental animal models of pulmonary hypertension: Development and challenges

Pulmonary hypertension (PH) is clinically divided into 5 major types, characterized by elevation in pulmonary arterial pressure (PAP) and pulmonary vascular resistance (PVR), finally leading to right heart failure and death. The pathogenesis of this arteriopathy remains unclear, leaving it impossible to target pulmonary vascular remodeling and reverse the deterioration of right ventricular (RV) function. Different animal models have been designed to reflect the complex mechanistic origins and pathology of PH, roughly divided into 4 categories according to the modeling methods: non‐invasive models in vivo, invasive models in vivo, gene editing models, and multi‐means joint modeling. Though each model shares some molecular and pathological changes with different classes of human PH, in most cases the molecular etiology of human PH is poorly known. The appropriate use of classic and novel PH animal models is essential for the hunt of molecular targets to reverse severe phenotypes.

Protein biomarkers and risk scores in pulmonary arterial hypertension associated with ventricular septal defect: integration of multi-omics and validation

The molecular mechanisms underlying pulmonary arterial hypertension (PAH) in congenital ventricular septal defects (VSD) are unclear. We aimed to reveal molecular pathways and potential biomarkers by multi-omics analysis in VSD-PAH. Plasma from 160 children, including 120 VSD patients with/without PAH and 40 healthy children was studied by integrated proteomics, metabolomics, and bioinformatics analyses. Proteomics identified 107 differential proteins (DPs) between patients with/without PAH including significantly increased adiponectin (ADIPO), dopamine β-hydroxylase (DBH), alanyl membrane aminopeptidase (ANPEP), transferrin receptor 1, and glycoprotein Ib platelet α-subunit and decreased guanine nucleotide-binding protein Gs in VSD-PAH. Metabolomics discovered 191 differential metabolites between patients with/without PAH, including elevation of serotonin, taurine, creatine, sarcosine, and 2-oxobutanoate, and decrease of vanillylmandelic acid, 3,4-dihydroxymandelate, 15-keto-prostaglandin F_2α, fructose 6-phosphate, l-glutamine, dehydroascorbate, hydroxypyruvate, threonine, l-cystine, and 1-aminocyclopropane-1-carboxylate. The DPs were validated in a new cohort of patients (n = 80). Integrated analyses identified key pathways, including cAMP, ECM receptor interaction, AMPK, hypoxia-inducible factor 1, PI3K-Akt signaling pathways, and amino acid metabolisms. Increased plasma protein levels of DBH, ADIPO, and ANPEP were found to be independently associated with the occurrence of PAH, with a new total risk score from these three proteins developed for clinical diagnosis. In this integrated multi-omics analysis in VSD-PAH patients, we have, for the first time, found that VSD-PAH patients present important differential proteins, metabolites, and key pathways. We have developed a total risk score (based on the plasma concentration of DBH, ANPEP, and ADIPO) as a predictor of development of PAH in CHD-VSD patients. Therefore, these proteins may be used as biomarkers, and the new total risk score has significant clinical implications in the diagnosis of PAH.