Subclinical liver fibrosis in patients with idiopathic pulmonary fibrosis

Cardiovascular Structural and Functional Parameters in Idiopathic Pulmonary Fibrosis at Disease Diagnosis

INTRODUCTION

Prevalence of cardiac and vascular fibrosis in patients with Idiopathic Pulmonary Fibrosis (IPF) has not been extensively evaluated.

AIM

In this study, we aimed to evaluate the heart and vessels functional and structural properties in patients with IPF compared to healthy controls. An exploratory analysis regarding disease severity in IPF patients has been done.

METHODS

We enrolled 50 patients with IPF (at disease diagnosis before antifibrotic therapy initiation) and 50 controls matched for age and gender. Heart was evaluated through echocardiography and plasmatic NT-pro-brain natriuretic peptide that, together with patients' symptoms, allow to define the presence of Heart Failure (HF) and diastolic dysfunction. Vessels were evaluated through Flow Mediated Dilation (FMD - endothelial function) and Pulse Wave Velocity (PWV-arterial stiffness) RESULTS: Patients with IPF had a prevalence of diastolic disfunction of 83.8%, HF of 37.8% and vascular fibrosis of 76.6%. No statistically significant difference was observed in comparison to the control group who showed prevalence of diastolic disfunction, HF and vascular fibrosis of 67.3%, 24.5% and 84.8%, respectively. Disease severity seems not to affect PWV, FMD, diastolic dysfunction and HF.

CONCLUSIONS

Patients with IPF early in the disease course do not present a significant CV fibrotic involvement when compared with age- and sex-matched controls. Bigger and adequately powered studies are needed to confirm our preliminary data and longitudinal studies are required in order to understand the time of appearance and progression rate of heart and vascular involvement in IPF subjects.

Automatic liver segmentation and assessment of liver fibrosis using deep learning with MR T1-weighted images in rats

OBJECTIVES

To validate the performance of nnU-Net in segmentation and CNN in classification for liver fibrosis using T1-weighted images.

MATERIALS AND METHODS

In this prospective study, animal models of liver fibrosis were induced by injecting subcutaneously a mixture of Carbon tetrachloride and olive oil. A total of 99 male Wistar rats were successfully induced and underwent MR scanning with no contrast agent to get T1-weighted images. The regions of interest (ROIs) of the whole liver were delineated layer by layer along the liver edge by 3D Slicer. For segmentation task, all T1-weighted images were randomly divided into training and test cohorts in a ratio of 7:3. For classification, images containing the hepatic maximum diameter of every rat were selected and 80% images of no liver fibrosis (NLF), early liver fibrosis (ELF) and progressive liver fibrosis (PLF) stages were randomly selected for training, while the rest were used for testing. Liver segmentation was performed by the nnU-Net model. The convolutional neural network (CNN) was used for classification task of liver fibrosis stages. The Dice similarity coefficient was used to evaluate the segmentation performance of nnU-Net. Confusion matrix, ROC curve and accuracy were used to show the classification performance of CNN.

RESULTS

A total of 2628 images were obtained from 99 Wistar rats by MR scanning. For liver segmentation by nnU-Net, the Dice similarity coefficient in the test set was 0.8477. The accuracies of CNN in staging NLF, ELF and PLF were 0.73, 0.89 and 0.84, respectively. The AUCs were 0.76, 0.88 and 0.79, respectively.

CONCLUSION

The nnU-Net architecture is of high accuracy for liver segmentation and CNN for assessment of liver fibrosis with T1-weighted images.

Latent inter-organ mechanism of idiopathic pulmonary fibrosis unveiled by a generative computational approach

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive disease characterized by complex lung pathogenesis affecting approximately three million people worldwide. While the molecular and cellular details of the IPF mechanism is emerging, our current understanding is centered around the lung itself. On the other hand, many human diseases are the products of complex multi-organ interactions. Hence, we postulate that a dysfunctional crosstalk of the lung with other organs plays a causative role in the onset, progression and/or complications of IPF. In this study, we employed a generative computational approach to identify such inter-organ mechanism of IPF. This approach found unexpected molecular relatedness of IPF to neoplasm, diabetes, Alzheimer's disease, obesity, atherosclerosis, and arteriosclerosis. Furthermore, as a potential mechanism underlying this relatedness, we uncovered a putative molecular crosstalk system across the lung and the liver. In this inter-organ system, a secreted protein, kininogen 1, from hepatocytes in the liver interacts with its receptor, bradykinin receptor B1 in the lung. This ligand-receptor interaction across the liver and the lung leads to the activation of calmodulin pathways in the lung, leading to the activation of interleukin 6 and phosphoenolpyruvate carboxykinase 1 pathway across these organs. Importantly, we retrospectively identified several pre-clinical and clinical evidence supporting this inter-organ mechanism of IPF. In conclusion, such feedforward and feedback loop system across the lung and the liver provides a unique opportunity for the development of the treatment and/or diagnosis of IPF. Furthermore, the result illustrates a generative computational framework for machine-mediated synthesis of mechanisms that facilitates and complements the traditional experimental approaches in biomedical sciences.

Topological data analysis identifies molecular phenotypes of idiopathic pulmonary fibrosis

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a debilitating, progressive disease with a median survival time of 3-5 years. Diagnosis remains challenging and disease progression varies greatly, suggesting the possibility of distinct subphenotypes.

METHODS AND RESULTS

We analysed publicly available peripheral blood mononuclear cell expression datasets for 219 IPF, 411 asthma, 362 tuberculosis, 151 healthy, 92 HIV and 83 other disease samples, totalling 1318 patients. We integrated the datasets and split them into train (n=871) and test (n=477) cohorts to investigate the utility of a machine learning model (support vector machine) for predicting IPF. A panel of 44 genes predicted IPF in a background of healthy, tuberculosis, HIV and asthma with an area under the curve of 0.9464, corresponding to a sensitivity of 0.865 and a specificity of 0.89. We then applied topological data analysis to investigate the possibility of subphenotypes within IPF. We identified five molecular subphenotypes of IPF, one of which corresponded to a phenotype enriched for death/transplant. The subphenotypes were molecularly characterised using bioinformatic and pathway analysis tools identifying distinct subphenotype features including one which suggests an extrapulmonary or systemic fibrotic disease.

CONCLUSIONS

Integration of multiple datasets, from the same tissue, enabled the development of a model to accurately predict IPF using a panel of 44 genes. Furthermore, topological data analysis identified distinct subphenotypes of patients with IPF which were defined by differences in molecular pathobiology and clinical characteristics.

A review of liver dysfunction in the lung transplant patient

Liver dysfunction is an increasingly common finding in patients evaluated for lung transplantation. New or worsening dysfunction in the perioperative period, defined by presence of clinical ascites/encephalopathy, high model for end-stage liver disease (MELD) score, and/or independent diagnostic criteria, is associated with high short- and long-term mortality. Therefore, a thorough liver function assessment is necessary prior to listing for lung transplant. Unfortunately, identification and intraoperative monitoring remain the only options for prevention of disease progression with isolated lung transplantation. Combined lung and liver transplantation may provide an option for definitive long-term management in selecting patients with known liver disease at high risk for postoperative progression. However, experience with the combined operation is extremely limited and indications for combined lung and liver transplant remain unclear. Herein, we present a comprehensive literature review of patients with liver dysfunction undergoing lung transplantation with and without concurrent liver transplant in an effort to illuminate the risks, benefits, and clinical judgement surrounding decision to pursue combined lung-liver transplantation (CLLT). We also argue description of liver function is currently a weakness of the current lung allocation scoring system. Additional algorithms incorporating liver function may aid in risk stratification and decision to pursue combined transplantation.