Endovascular Abdominal Aortic Aneurysm Repair: Overview of Current Guidance, Strategies, and New Technologies, Perspectives from the United Kingdom.

Endovascular aortic aneurysm repair has changed the management of patients affected by this condition, offering a minimally invasive solution with satisfactory outcomes. Constant evolution of this technology has expanded the use of endovascular devices to more complex cases. The purpose of this review article is to describe the current strategies, guidance, and technologies in this field, with a particular focus on practices in the United Kingdom.

Tropoelastin: an in vivo imaging marker of dysfunctional matrix turnover during abdominal aortic dilation.

AIMS: Dysfunctional matrix turnover is present at sites of abdominal aortic aneurysm (AAA) and leads to the accumulation of monomeric tropoelastin rather than cross-linked elastin. We used a gadolinium-based tropoelastin-specific magnetic resonance contrast agent (Gd-TESMA) to test whether quantifying regional tropoelastin turnover correlates with aortic expansion in a murine model. The binding of Gd-TESMA to excised human AAA was also assessed. METHODS AND RESULTS: We utilized the angiotensin II (Ang II)-infused apolipoprotein E gene knockout (ApoE-/-) murine model of aortic dilation and performed in vivo imaging of tropoelastin by administering Gd-TESMA followed by late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) and T1 mapping at 3 T, with subsequent ex vivo validation. In a cross-sectional study (n = 66; control = 11, infused = 55) we found that Gd-TESMA enhanced MRI was elevated and confined to dilated aortic segments (control: LGE=0.13 ± 0.04 mm2, control R1= 1.1 ± 0.05 s-1 vs. dilated LGE =1.0 ± 0.4 mm2, dilated R1 =2.4 ± 0.9 s-1) and was greater in segments with medium (8.0 ± 3.8 mm3) and large (10.4 ± 4.1 mm3) compared to small (3.6 ± 2.1 mm3) vessel volume. Furthermore, a proof-of-principle longitudinal study (n = 19) using Gd-TESMA enhanced MRI demonstrated a greater proportion of tropoelastin: elastin expression in dilating compared to non-dilating aortas, which correlated with the rate of aortic expansion. Treatment with pravastatin and aspirin (n = 10) did not reduce tropoelastin turnover (0.87 ± 0.3 mm2 vs. 1.0 ± 0.44 mm2) or aortic dilation (4.86 ± 2.44 mm3 vs. 4.0 ± 3.6 mm3). Importantly, Gd-TESMA-enhanced MRI identified accumulation of tropoelastin in excised human aneurysmal tissue (n = 4), which was confirmed histologically. CONCLUSION: Tropoelastin MRI identifies dysfunctional matrix remodelling that is specifically expressed in regions of aortic aneurysm or dissection and correlates with the development and rate of aortic expansion. Thus, it may provide an additive imaging marker to the serial assessment of luminal diameter for surveillance of patients at risk of or with established aortopathy.

Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks.

Purpose To develop and test an artificial intelligence (AI) system, based on deep convolutional neural networks (CNNs), for automated real-time triaging of adult chest radiographs on the basis of the urgency of imaging appearances. Materials and Methods An AI system was developed by using 470 388 fully anonymized institutional adult chest radiographs acquired from 2007 to 2017. The free-text radiology reports were preprocessed by using an in-house natural language processing (NLP) system modeling radiologic language. The NLP system analyzed the free-text report to prioritize each radiograph as critical, urgent, nonurgent, or normal. An AI system for computer vision using an ensemble of two deep CNNs was then trained by using labeled radiographs to predict the clinical priority from radiologic appearances only. The system's performance in radiograph prioritization was tested in a simulation by using an independent set of 15 887 radiographs. Prediction performance was assessed with the area under the receiver operating characteristic curve; sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also determined. Nonparametric testing of the improvement in time to final report was determined at a nominal significance level of 5%. Results Normal chest radiographs were detected by our AI system with a sensitivity of 71%, specificity of 95%, PPV of 73%, and NPV of 94%. The average reporting delay was reduced from 11.2 to 2.7 days for critical imaging findings (P < .001) and from 7.6 to 4.1 days for urgent imaging findings (P < .001) in the simulation compared with historical data. Conclusion Automated real-time triaging of adult chest radiographs with use of an artificial intelligence system is feasible, with clinically acceptable performance. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Auffermann in this issue.

Learning to detect chest radiographs containing pulmonary lesions using visual attention networks.

Machine learning approaches hold great potential for the automated detection of lung nodules on chest radiographs, but training algorithms requires very large amounts of manually annotated radiographs, which are difficult to obtain. The increasing availability of PACS (Picture Archiving and Communication System), is laying the technological foundations needed to make available large volumes of clinical data and images from hospital archives. Binary labels indicating whether a radiograph contains a pulmonary lesion can be extracted at scale, using natural language processing algorithms. In this study, we propose two novel neural networks for the detection of chest radiographs containing pulmonary lesions. Both architectures make use of a large number of weakly-labelled images combined with a smaller number of manually annotated x-rays. The annotated lesions are used during training to deliver a type of visual attention feedback informing the networks about their lesion localisation performance. The first architecture extracts saliency maps from high-level convolutional layers and compares the inferred position of a lesion against the true position when this information is available; a localisation error is then back-propagated along with the softmax classification error. The second approach consists of a recurrent attention model that learns to observe a short sequence of smaller image portions through reinforcement learning; the reward function penalises the exploration of areas, within an image, that are unlikely to contain nodules. Using a repository of over 430,000 historical chest radiographs, we present and discuss the proposed methods over related architectures that use either weakly-labelled or annotated images only.