Protective Role for Smooth Muscle Cell Hepcidin in Abdominal Aortic Aneurysm.

BACKGROUND: Hepcidin is a liver-derived hormone that controls systemic iron homeostasis, by inhibiting the iron exporter ferroportin in the gut and spleen, respective sites of iron absorption and recycling. Hepcidin is also expressed ectopically in the context of cardiovascular disease. However, the precise role of ectopic hepcidin in underlying pathophysiology is unknown. In patients with abdominal aortic aneurysm (AAA), hepcidin is markedly induced in smooth muscle cells (SMCs) of the aneurysm wall and inversely correlated with the expression of LCN2 (lipocalin-2), a protein implicated in AAA pathology. In addition, plasma hepcidin levels were inversely correlated with aneurysm growth, suggesting hepcidin has a potential disease-modifying role. METHODS: To probe the role of SMC-derived hepcidin in the setting of AAA, we applied AngII (Angiotensin-II)-induced AAA model to mice harbouring an inducible, SMC-specific deletion of hepcidin. To determine whether SMC-derived hepcidin acted cell-autonomously, we also used mice harboring an inducible SMC-specific knock-in of hepcidin-resistant ferroportinC326Y. The involvement of LCN2 was established using a LCN2-neutralizing antibody. RESULTS: Mice with SMC-specific deletion of hepcidin or knock-in of hepcidin-resistant ferroportinC326Y had a heightened AAA phenotype compared with controls. In both models, SMCs exhibited raised ferroportin expression and reduced iron retention, accompanied by failure to suppress LCN2, impaired autophagy in SMCs, and greater aortic neutrophil infiltration. Pretreatment with LCN2-neutralizing antibody restored autophagy, reduced neutrophil infiltration, and prevented the heightened AAA phenotype. Finally, plasma hepcidin levels were consistently lower in mice with SMC-specific deletion of hepcidin than in controls, indicating that SMC-derived hepcidin contributes to the circulating pool in AAA. CONCLUSIONS: Hepcidin elevation in SMCs plays a protective role in the setting of AAA. These findings are the first demonstration of a protective rather than deleterious role for hepcidin in cardiovascular disease. They highlight the need to further explore the prognostic and therapeutic value of hepcidin outside disorders of iron homeostasis.

Prediction of Abdominal Aortic Aneurysm Growth Using Geometric Assessment of Computerized Tomography Images Acquired During the Aneurysm Surveillance Period.

OBJECTIVE: We investigated the utility of geometric features for future AAA growth prediction. BACKGROUND: Novel methods for growth prediction of AAA are recognized as a research priority. Geometric feature have been used to predict cerebral aneurysm rupture, but not examined as predictor of AAA growth. METHODS: Computerized tomography (CT) scans from patients with infra-renal AAAs were analyzed. Aortic volumes were segmented using an automated pipeline to extract AAA diameter (APD), undulation index (UI), and radius of curvature (RC). Using a prospectively recruited cohort, we first examined the relation between these geometric measurements to patients' demographic features (n = 102). A separate 192 AAA patients with serial CT scans during AAA surveillance were identified from an ongoing clinical database. Multinomial logistic and multiple linear regression models were trained and optimized to predict future AAA growth in these patients. RESULTS: There was no correlation between the geometric measurements and patients' demographic features. APD (Spearman r = 0.25, P < 0.05), UI (Spearman r = 0.38, P < 0.001) and RC (Spearman r =-0.53, P < 0.001) significantly correlated with annual AAA growth. Using APD, UI, and RC as 3 input variables, the area under receiver operating characteristics curve for predicting slow growth (<2.5 mm/yr) or fast growth (>5 mm/yr) at 12 months are 0.80 and 0.79, respectively. The prediction or growth rate is within 2 mm error in 87% of cases. CONCLUSIONS: Geometric features of an AAA can predict its future growth. This method can be applied to routine clinical CT scans acquired from patients during their AAA surveillance pathway.

A Deep Learning Approach to Visualize Aortic Aneurysm Morphology Without the Use of Intravenous Contrast Agents.

BACKGROUND: Intravenous contrast agents are routinely used in CT imaging to enable the visualization of intravascular pathology, such as with abdominal aortic aneurysms. However, the injection is contraindicated in patients with iodine allergy and is associated with renal complications. OBJECTIVES: In this study, we investigate if the raw data acquired from a noncontrast CT image contains sufficient information to differentiate blood and other soft tissue components. A deep learning pipeline underpinned by generative adversarial networks was developed to simulate contrast enhanced CTA images using noncontrast CTs. METHODS AND RESULTS: Two generative models (cycle- and conditional) are trained with paired noncontrast and contrast enhanced CTs from seventy-five patients (total of 11,243 pairs of images) with abdominal aortic aneurysms in a 3-fold cross-validation approach with a training/testing split of 50:25 patients. Subsequently, models were evaluated on an independent validation cohort of 200 patients (total of 29,468 pairs of images). Both deep learning generative models are able to perform this image transformation task with the Cycle-generative adversarial network (GAN) model outperforming the Conditional-GAN model as measured by aneurysm lumen segmentation accuracy (Cycle-GAN: 86.1% ± 12.2% vs Con-GAN: 85.7% ± 10.4%) and thrombus spatial morphology classification accuracy (Cycle-GAN: 93.5% vs Con-GAN: 85.7%). CONCLUSION: This pipeline implements deep learning methods to generate CTAs from noncontrast images, without the need of contrast injection, that bear strong concordance to the ground truth and enable the assessment ofimportant clinical metrics. Our pipeline is poised to disrupt clinical pathways requiring intravenous contrast.

A Deep Learning Pipeline to Automate High-Resolution Arterial Segmentation With or Without Intravenous Contrast.

BACKGROUND: Existing methods to reconstruct vascular structures from a computerized tomography (CT) angiogram rely on contrast injection to enhance the radio-density within the vessel lumen. However, pathological changes in the vasculature may be present that prevent accurate reconstruction. In aortic aneurysmal disease, a thrombus adherent to the aortic wall within the expanding aneurysmal sac is present in >90% of cases. These deformations prevent the automatic extraction of vital clinical information by existing image reconstruction methods. AIM: In this study, a deep learning architecture consisting of a modified U-Net with attention-gating was implemented to establish a high-throughput and automated segmentation pipeline of pathological blood vessels in CT images acquired with or without the use of a contrast agent. METHODS AND RESULTS: Seventy-Five patients with paired noncontrast and contrast-enhanced CT images were randomly selected from an ongoing study (Ethics Ref 13/SC/0250), manually annotated and used for model training and evaluation. Data augmentation was implemented to diversify the training data set in a ratio of 10:1. The performance of our Attention-based U-Net in extracting both the inner (blood flow) lumen and the wall structure of the aortic aneurysm from CT angiograms was compared against a generic 3-D U-Net and displayed superior results. Implementation of this network within the aortic segmentation pipeline for both contrast and noncontrast CT images has allowed for accurate and efficient extraction of the morphological and pathological features of the entire aortic volume. CONCLUSIONS: This extraction method can be used to standardize aneurysmal disease management and sets the foundation for complex geometric and morphological analysis. Furthermore, this pipeline can be extended to other vascular pathologies.

Integrated Plasma and Tissue Proteomics Reveals Attractin Release by Intraluminal Thrombus of Abdominal Aortic Aneurysms and Improves Aneurysm Growth Prediction in Humans.

OBJECTIVE: Discovery of novel biomarkers for AAA growth prediction. BACKGROUND: Novel biomarker of AAA growth is a recognized priority in research. Our prior work implicated intraluminal thrombus (ILT) in AAAs to be a potential source of systemic mediators during AAA progression. Here we applied a mass spectrometry proteomics pipeline to discover novel biomarkers for AAA growth prediction. METHODS: Patients were prospectively recruited. Plasma samples were collected at baseline (n = 62). AAA growth was recorded at 12 months. In Experiment 1, plasma samples from the fastest and slowest growth patients (n = 10 each) were compared. In Experiment 2, plasma samples were collected before and at 10-12 weeks after surgery (n = 29). In Experiment 3, paired ILT and omental biopsies were collected intra-operatively during open surgical repair (n = 3). In Experiment 4, tissue secretome was obtained from ex-vivo culture of these paired tissue samples. Samples were subjected to a liquid chromatography tandem mass spectrometry workflow to discover novel biomarkers. RESULTS: We discovered 3 proteins that are: (i) present in ILT; (ii) released by ILT; (iii) reduced in circulation after AAA surgery; (iv) differs between fast and slow growth AAAs. One of these is Attractin. Plasma Attractin correlates significantly with future AAA growth (Spearman r = 0.35, P < 0.005). Using Attractin and AAA diameter as input variables, the area under receiver operating characteristics for predicting no growth and fast growth or AAA at 12 months is 85% and 76%, respectively. CONCLUSION: We show that ILT of AAAs releases mediators during the natural history of AAA growth. These are novel biomarkers for AAA growth prediction in humans.

The role of dipeptidyl peptidase-IV in abdominal aortic aneurysm pathogenesis: A systematic review.

Abdominal aortic aneurysm (AAA) is an important vascular disease carrying significant mortality implications due to the risk of aneurysm rupture. Current management relies exclusively on surgical repair as there is no effective medical therapy. A key element of AAA pathogenesis is the chronic inflammation mediated by inflammatory cells releasing proteases, including the enzyme dipeptidyl peptidase IV (DPP-IV). This review sought to recapitulate available evidence on the involvement of DPP-IV in AAA development. Further, we assessed the experimental use of currently available DPP-IV inhibitors for AAA management in murine models. Embase, Medline, PubMed, and Web of Science databases were utilised to access the relevant studies. The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). A narrative synthesis approach was used. Sixty-four studies were identified from the searched databases; a final 11 were included in the analysis. DPP-IV was reported to be significantly increased in both AAA tissue and plasma of patients and correlated with AAA growth. DPP-IV inhibitors (sitagliptin, vildagliptin, alogliptin, and teneligliptin) were all shown to attenuate AAA formation in murine models by reducing monocyte differentiation, the release of reactive oxygen species (ROS), and metalloproteinases (MMP-2 and MMP-9). DPP-IV seems to play a role in AAA pathogenesis by propagating the inflammatory microenvironment. This is supported by observations of decreased AAA formation and reduction in macrophage infiltration, ROS, matrix MMPs, and interleukins following the use of DPP-IV inhibitors in murine models. There is an existing translational gap from preclinical observations to clinical trials in this important and novel mechanism of AAA pathogenesis. This prior literature highlights the need for further research on molecular targets involved in AAA formation.

Use of deep learning for detection, characterisation and prediction of metastatic disease from computerised tomography: a systematic review.

CT is widely used for diagnosis, staging and management of cancer. The presence of metastasis has significant implications on treatment and prognosis. Deep learning (DL), a form of machine learning, where layers of programmed algorithms interpret and recognise patterns, may have a potential role in CT image analysis. This review aims to provide an overview on the use of DL in CT image analysis in the diagnostic evaluation of metastatic disease. A total of 29 studies were included which could be grouped together into three areas of research: the use of deep learning on the detection of metastatic disease from CT imaging, characterisation of lesions on CT into metastasis and prediction of the presence or development of metastasis based on the primary tumour. In conclusion, DL in CT image analysis could have a potential role in evaluating metastatic disease; however, prospective clinical trials investigating its clinical value are required.

The Spatial Morphology of Intraluminal Thrombus Influences Type II Endoleak after Endovascular Repair of Abdominal Aortic Aneurysms.

INTRODUCTION: Type 2 endoleaks (T2Es) after endovascular repair (EVAR) of abdominal aortic aneurysm (AAA) can lead to sac expansion or failure of sac regression, and often present as a management dilemma. The intraluminal thrombus (ILT) may influence the likelihood of endoleaks after EVAR and can be characterized using routine preoperative imaging. We examined the relationship between preoperative spatial morphology of ILT and the incidence of postoperative T2E. METHODS: All patients who underwent EVAR at the John Radcliffe Hospital (Oxford, UK) were prospectively entered in a clinical database. Computed tomography angiograms (CTAs) were performed as part of routine clinical care. The ILT morphology of each patient was determined using the preoperative CTA. Arterial phase cross-sectional images of the AAA were analyzed according to the presence and morphology of the thrombus in each quadrant. The overall ILT morphology was defined by measurements obtained over a 4-cm segment of the AAA. The diagnosis of T2Es during EVAR surveillance was confirmed by CTAs. The relation between the ILT morphology and T2E was assessed using logistic regression. RESULTS: Between September 2009 and July 2016, 271 patients underwent EVAR for infrarenal AAAs (male: 241, age = 79 ± 7). The ILT was present in 265 (98%) of AAAs. Mean follow-up was 1.9 ± 1.6 years. The T2E was observed in 77 cases. Sixty-one percent of T2Es were observed within the first week after surgery. The T2E was observed in 50% (3/6) of cases without the ILT (no-ILT). Compared with no-ILT, the presence of circumferential or posterolateral ILTs was protective from T2Es (odds ratio = 0.33 and 0.37; P = 0.002 and P = 0.047, respectively). CONCLUSIONS: The spatial ILT morphology on routine preoperative CTA imaging can be a biomarker for post-EVAR T2Es. ILTs that cover the posterolateral aspects of the lumen, or circumferential ILTs, are protective of T2Es. This information can be useful in the preoperative planning of EVARs.

Mechanical and Biochemical Role of Fibrin Within a Venous Thrombus.

BACKGROUND: The physical properties of a venous thrombus are derived from molecular characteristics, including fibrin polymer diameter, density, branching, and cross-linking. Dense thrombi with thin, highly branched fibrin fibres and small pores in the meshwork have been observed to be more rigid, less permeable, and more resistant to lysis. The three dimensional fibrin meshwork acts as the main structure to entrap and capture erythrocytes, platelets and plasma components. Attached factors become integrated into the developing thrombus, co-localise with fibrin deposition and act in either a pro- or anticoagulant capacity. Similarly, factors including blood flow, osmolarity and pH, oxidative stress, platelet and leukocyte recruitment, and thrombin concentration alter thrombus composition, architecture, and its mechanical properties. CONCLUSIONS: Over time, an increase in thrombus cellular composition and a linear decrease in fibrin content as a function of thrombus age is observed. However, little else is known regarding the evolution of fibrin based clots. The role of fibrin in mediating cellular coordination, thrombus maturation, and changes of the venous wall also requires further research. This review discusses the current impact of fibrin on thrombus remodeling and addresses the limitations of the work done in this area.

A deep learning pipeline to simulate fluorodeoxyglucose (FDG) uptake in head and neck cancers using non-contrast CT images without the administration of radioactive tracer.

OBJECTIVES: Positron emission tomography (PET) imaging is a costly tracer-based imaging modality used to visualise abnormal metabolic activity for the management of malignancies. The objective of this study is to demonstrate that non-contrast CTs alone can be used to differentiate regions with different Fluorodeoxyglucose (FDG) uptake and simulate PET images to guide clinical management. METHODS: Paired FDG-PET and CT images (n = 298 patients) with diagnosed head and neck squamous cell carcinoma (HNSCC) were obtained from The cancer imaging archive. Random forest (RF) classification of CT-derived radiomic features was used to differentiate metabolically active (tumour) and inactive tissues (ex. thyroid tissue). Subsequently, a deep learning generative adversarial network (GAN) was trained for this CT to PET transformation task without tracer injection. The simulated PET images were evaluated for technical accuracy (PERCIST v.1 criteria) and their ability to predict clinical outcome [(1) locoregional recurrence, (2) distant metastasis and (3) patient survival]. RESULTS: From 298 patients, 683 hot spots of elevated FDG uptake (elevated SUV, 6.03 ± 1.71) were identified. RF models of intensity-based CT-derived radiomic features were able to differentiate regions of negligible, low and elevated FDG uptake within and surrounding the tumour. Using the GAN-simulated PET image alone, we were able to predict clinical outcome to the same accuracy as that achieved using FDG-PET images. CONCLUSION: This pipeline demonstrates a deep learning methodology to simulate PET images from CT images in HNSCC without the use of radioactive tracer. The same pipeline can be applied to other pathologies that require PET imaging.

Development of a novel index to characterise arterial dynamics using ultrasound imaging.

Abdominal aortic aneurysms (AAA) are associated with systemic inflammation and endothelial dysfunction. We previously reported flow mediated dilatation (FMD) of the brachial artery as a predictor of AAA growth. We hence hypothesised that other physical characteristics of the brachial artery correlate with AAA growth. Using a prospectively cohort of AAA patients, we devised a 'brachial artery relaxation index' (BARI) and examined its role as a biomarker for AAA growth. However, no correlation between BARI and future aneurysm growth was observed (p = 0.45). Therefore, our investigations did not substantiate the hypothesis that other physical characteristics of the brachial artery predicts AAA growth.

Shared decision making and consent post-Montgomery, UK Supreme Court judgement supporting best practice.

The UK Supreme Court Montgomery judgement marks a decisive shift in the legal test of duty of care in the context of consent to treatment from the perspective of the clinician (as represented by Bolam rules) to that of the patient. This has important implications in the surgical field worldwide, where informed consent is critical. This paper aims to explain the ruling and how it impacts the consent process. The case and ruling are outlined and summarised as pertaining to consent and requirements for validity; a shift from the clinician's interpretation about what would be best for patients to the values of the particular patient concerned in the decision in question. A sample of recent commentaries is reviewed. Four examples illustrate some of the practical applications of the Montgomery ruling on consent and how the ruling can empower doctors and patients to make mutually beneficial shared decisions. Future consent should be obtained using a Montgomery compliant strategy in accordance with the principles of shared decision making.

Lack of symmetry in the major lower limb veins.

BACKGROUND: Contemporary texts frequently present the venous system of the lower limb as a prime example of bilateral symmetry. However, overt bilateral asymmetry may be noted. This study was designed to examine and to quantify the level of symmetry in the lower extremity veins. METHODS: This prospective cohort study evaluated major anatomic differences between right and left lower extremity veins in adult patients. Two hundred patients presenting with signs and symptoms of chronic venous disease (CVD; class 2-6) and venous reflux on duplex ultrasound were examined. A second group of 25 healthy volunteers without reflux or obstruction were used as controls. Those with conditions that could potentially alter vasculature, including vascular malformation, lower extremity trauma, and previously documented surgery for venous disease or bypass operations, were excluded. Only overt changes in the main superficial veins (great saphenous and small saphenous veins) and deep veins (femoral, popliteal, peroneal, and tibial veins) of the lower extremities were examined for symmetry. The level of small saphenous vein termination and thigh extension veins were also included. Location and extent of hypoplasia or aplasia and venous duplication or triplication were noted for all venous segments. RESULTS: Of the 100 patients (200 limbs) with CVD class 2-3 (age, 49 years; range, 21-78 years), the extent of asymmetry in the superficial system, deep system, and combined was 84%, 86%, and 100%, respectively. Similarly, of the 100 patients (200 limbs) with CVD class 4-6 (age, 56 years; range, 28-84 years), the level of asymmetry in the superficial system, deep system, and combined was 83%, 84%, and 100%, respectively. Fifty (100 limbs) healthy volunteers (age, 46 years; range, 18-74 years) also presented similarly elevated levels of asymmetry compared with both cohorts of differing CVD class severity (superficial, 78%; deep, 84%; both, 100%). The most common reason for asymmetry in the great saphenous vein was hypoplasia or aplasia, whereas duplication was rare. In the small saphenous vein, the level of termination, thigh extension, and hypoplasia were the more frequent reasons. In the deep veins, duplication of the femoral and popliteal veins at different locations and extents was the most common finding for asymmetry. These findings remained consistent for all subgroups of patients and healthy controls. CONCLUSIONS: A complete lack of symmetry is consistently seen in CVD patients and healthy subjects. Asymmetry in the veins of the lower extremities appears to be the norm. This is true despite examination of only overt changes and exclusion of multiple veins from the comparison.