Evaluation of automated airway morphological quantification for assessing fibrosing lung disease

Abnormal airway dilatation, termed traction bronchiectasis, is a typical feature of idiopathic pulmonary fibrosis (IPF). Volumetric computed tomography (CT) imaging captures the loss of normal airway tapering in IPF. We postulated that automated quantification of airway abnormalities could provide estimates of IPF disease extent and severity. We propose AirQuant, an automated computational pipeline that systematically parcellates the airway tree into its lobes and generational branches from a deep learning based airway segmentation, deriving airway structural measures from chest CT. Importantly, AirQuant prevents the occurrence of spurious airway branches by thick wave propagation and removes loops in the airway-tree by graph search, overcoming limitations of existing airway skeletonisation algorithms. Tapering between airway segments (intertapering) and airway tortuosity computed by AirQuant were compared between 14 healthy participants and 14 IPF patients. Airway intertapering was significantly reduced in IPF patients, and airway tortuosity was significantly increased when compared to healthy controls. Differences were most marked in the lower lobes, conforming to the typical distribution of IPF-related damage. AirQuant is an open-source pipeline that avoids limitations of existing airway quantification algorithms and has clinical interpretability. Automated airway measurements may have potential as novel imaging biomarkers of IPF severity and disease extent

Evaluation of automated airway morphological quantification for assessing fibrosing lung disease

Abnormal airway dilatation, termed traction bronchiectasis, is a typical feature of idiopathic pulmonary fibrosis (IPF). Volumetric computed tomography (CT) imaging captures the loss of normal airway tapering in IPF. We postulated that automated quantification of airway abnormalities could provide estimates of IPF disease extent and severity. We propose AirQuant, an automated computational pipeline that systematically parcellates the airway tree into its lobes and generational branches from a deep learning based airway segmentation, deriving airway structural measures from chest CT. Importantly, AirQuant prevents the occurrence of spurious airway branches by thick wave propagation and removes loops in the airway-tree by graph search, overcoming limitations of existing airway skeletonisation algorithms. Tapering between airway segments (intertapering) and airway tortuosity computed by AirQuant were compared between 14 healthy participants and 14 IPF patients. Airway intertapering was significantly reduced in IPF patients, and airway tortuosity was significantly increased when compared to healthy controls. Differences were most marked in the lower lobes, conforming to the typical distribution of IPF-related damage. AirQuant is an open-source pipeline that avoids limitations of existing airway quantification algorithms and has clinical interpretability. Automated airway measurements may have potential as novel imaging biomarkers of IPF severity and disease extent

Real-time metabolic profiling of oesophageal tumours reveals an altered metabolic phenotype to different oxygen tensions and to treatment with Pyrazinib

Abstract Oesophageal cancer is the 6th most common cause of cancer related death worldwide. The current standard of care for oesophageal adenocarcinoma (OAC) focuses on neoadjuvant therapy with chemoradiation or chemotherapy, however the 5-year survival rates remain at < 20%. To improve treatment outcomes it is critical to further investigate OAC tumour biology, metabolic phenotype and their metabolic adaptation to different oxygen tensions. In this study, by using human ex-vivo explants we demonstrated using real-time metabolic profiling that OAC tumour biopsies have a significantly higher oxygen consumption rate (OCR), a measure of oxidative phosphorylation compared to extracellular acidification rate (ECAR), a measure of glycolysis (p = 0.0004). Previously, we identified a small molecule compound, pyrazinib which enhanced radiosensitivity in OAC. Pyrazinib significantly inhibited OCR in OAC treatment-naïve biopsies (p = 0.0139). Furthermore, OAC biopsies can significantly adapt their metabolic rate in real-time to their environment. Under hypoxic conditions pyrazinib produced a significant reduction in both OCR (p = 0.0313) and ECAR in OAC treatment-naïve biopsies. The inflammatory secretome profile from OAC treatment-naïve biopsies is heterogeneous. OCR was positively correlated with three secreted factors in the tumour conditioned media: vascular endothelial factor A (VEGF-A), IL-1RA and thymic stromal lymphopoietin (TSLP). Pyrazinib significantly inhibited IL-1β secretion (p = 0.0377) and increased IL-3 (p = 0.0020) and IL-17B (p = 0.0181). Importantly, pyrazinib did not directly alter the expression of dendritic cell maturation markers or reduce T-cell viability or activation markers. We present a new method for profiling the metabolic rate of tumour biopsies in real-time and demonstrate the novel anti-metabolic and anti-inflammatory action of pyrazinib ex-vivo in OAC tumours, supporting previous findings in-vitro whereby pyrazinib significantly enhanced radiosensitivity in OAC

Real-time metabolic profiling of oesophageal tumours reveals an altered metabolic phenotype to different oxygen tensions and to treatment with Pyrazinib

Oesophageal cancer is the 6th most common cause of cancer related death worldwide. The current standard of care for oesophageal adenocarcinoma (OAC) focuses on neoadjuvant therapy with chemoradiation or chemotherapy, however the 5-year survival rates remain a

Mucosal-Associated Invariant T Cells Display Diminished Effector Capacity in Oesophageal Adenocarcinoma

Oesophageal adenocarcinoma (OAC) is an aggressive malignancy with poor prognosis, and incidence is increasing rapidly in the Western world. Mucosal-associated invariant T (MAIT) cells recognize bacterial metabolites and kill infected cells, yet their role in OAC is unknown. We aimed to elucidate the role of MAIT cells during cancer development by characterizing the frequency, phenotype, and function of MAIT cells in human blood and tissues, from OAC and its pre-malignant inflammatory condition Barrett’s oesophagus (BO). Blood and tissues were phenotyped by flow cytometry and conditioned media from explanted tissue was used to model the effects of the tumor microenvironment on MAIT cell function. Associations were assessed between MAIT cell frequency, circulating inflammatory markers, and clinical parameters to elucidate the role of MAIT cells in inflammation driven cancer. MAIT cells were decreased in BO and OAC blood compared to healthy controls, but were increased in oesophageal tissues, compared to BO-adjacent tissue, and remained detectable after neo-adjuvant treatment. MAIT cells in tumors expressed CD8, PD-1, and NKG2A but lower NKG2D than BO cohorts. MAIT cells produced less IFN-γ and TNF-α in the presence of tumor-conditioned media. OAC cell line viability was reduced upon exposure to expanded MAIT cells. Serum levels of chemokine IP-10 were inversely correlated with MAIT cell frequency in both tumors and blood. MAIT cells were higher in the tumors of node-negative patients, but were not significantly associated with other clinical parameters. This study demonstrates that OAC tumors are infiltrated by MAIT cells, a type of CD8 T cell featuring immune checkpoint expression and cytotoxic potential. These findings may have implications for immunotherapy and immune scoring approaches

Risk factors for pleural effusion recurrence in patients with malignancy

BACKGROUND AND OBJECTIVE: The main purpose of treatment in patients with malignant pleural effusion (MPE) is symptom palliation. Currently, patients undergo repeat thoracenteses prior to receiving a definitive procedure as clinicians are not aware of the risk factors associated with fluid recurrence. The primary objective of this study was to identify risk factors associated with recurrent symptomatic MPE. METHODS: Retrospective multicentre cohort study of patients who underwent first thoracentesis was performed. The primary outcome was time to fluid recurrence requiring intervention in patients with evidence of metastatic disease. We used a cause-specific hazard model to identify risk factors associated with fluid recurrence. We also developed a predictive model, utilizing Fine-Gray subdistribution hazard model, and externally validated the model. RESULTS: A total of 988 patients with diagnosed metastatic disease were included. Cumulative incidence of recurrence was high with 30% of patients recurring by day 15. On multivariate analysis, size of the effusion on chest X-ray (up to the top of the cardiac silhouette (hazard ratio (HR): 1.84, 95% CI: 1.21-2.80, P = 0.004) and above the cardiac silhouette (HR: 2.22, 95% CI: 1.43-3.46, P = 0.0004)), larger amount of pleural fluid drained (HR: 1.06, 95% CI: 1.04-1.07, P \u3c 0.0001) and higher pleural fluid LDH (HR: 1.008, 95% CI: 1.004-1.011, P \u3c 0.0001) were associated with increased hazard of recurrence. Negative cytology (HR: 0.52, 95% CI: 0.43-0.64, P \u3c 0.0001) was associated with decreased hazard of recurrence. The model had low prediction accuracy. CONCLUSION: Pleural effusion size, amount of pleural fluid drained, LDH and pleural fluid cytology were found to be risk factors for recurrence