Non-Invasive Imaging of Coronary Artery Disease — The Expanding Role of Coronary Computed Tomographic Angiography in the Management of Low- to Intermediate-Risk Patients and Dealing with Intermediate Stenosis

Non-invasive anatomic imaging modalities play a crucial role in the diagnosis of coronary artery disease (CAD), particularly in the case of the symptomatic patient presenting in the emergency department

Multimodality imaging of Abernethy malformation

Abernethy malformation, or congenital extrahepatic portosystemic venous shunt, is a rare anomaly involving the portal venous system. Despite its rarity, it is increasingly being reported, and therefore, it is important to diagnose given the potential adverse clinical consequences if left untreated. It has a spectrum of presentations, ranging from complete lack of symptoms, to causing hepatic carcinoma, hepatic encephalopathy, severe pulmonary hypertension, and diffuse pulmonary arteriovenous malformation. We herein describe the case and echocardiographic, computed tomography, and magnetic resonance imaging findings of a transgender individual, with this anomaly detected incidentally during adulthood

Assessing the role of an artificial intelligence assessment tool for thoracic aorta diameter on routine chest CT

OBJECTIVE: To assess the diagnostic accuracy and clinical impact of automated artificial intelligence (AI) measurement of thoracic aorta diameter on routine chest CT. METHODS: A single-centre retrospective study involving three cohorts. 210 consecutive ECG-gated CT aorta scans (mean age 75 ± 13) underwent automated analysis (AI-Rad Companion Chest CT, Siemens) and were compared to a reference standard of specialist cardiothoracic radiologists for accuracy measuring aortic diameter. A repeated measures analysis tested reporting consistency in a second cohort (29 patients, mean age 61 ± 17) of immediate sequential pre-contrast and contrast CT aorta acquisitions. Potential clinical impact was assessed in a third cohort of 197 routine CT chests (mean age 66 ± 15) to document potential clinical impact. RESULTS: AI analysis produced a full report in 387/436 (89%) and a partial report in 421/436 (97%). Manual vs AI agreement was good to excellent (ICC 0.76-0.92). Repeated measures analysis of expert and AI reports for the ascending aorta were moderate to good (ICC 0.57-0.88). AI diagnostic performance crossed the threshold for maximally accepted limits of agreement (&gt;5 mm) at the aortic root on ECG-gated CTs. AI newly identified aortic dilatation in 27% of patients on routine thoracic imaging with a specificity of 99% and sensitivity of 77%. CONCLUSION: AI has good agreement with expert readers at the mid-ascending aorta and has high specificity, but low sensitivity, at detecting dilated aortas on non-dedicated chest CTs. ADVANCES IN KNOWLEDGE: An AI tool may improve the detection of previously unknown thoracic aorta dilatation on chest CTs vs current routine reporting.</p

Assessing the role of an artificial intelligence assessment tool for thoracic aorta diameter on routine chest CT

OBJECTIVE: To assess the diagnostic accuracy and clinical impact of automated artificial intelligence (AI) measurement of thoracic aorta diameter on routine chest CT. METHODS: A single-centre retrospective study involving three cohorts. 210 consecutive ECG-gated CT aorta scans (mean age 75 ± 13) underwent automated analysis (AI-Rad Companion Chest CT, Siemens) and were compared to a reference standard of specialist cardiothoracic radiologists for accuracy measuring aortic diameter. A repeated measures analysis tested reporting consistency in a second cohort (29 patients, mean age 61 ± 17) of immediate sequential pre-contrast and contrast CT aorta acquisitions. Potential clinical impact was assessed in a third cohort of 197 routine CT chests (mean age 66 ± 15) to document potential clinical impact. RESULTS: AI analysis produced a full report in 387/436 (89%) and a partial report in 421/436 (97%). Manual vs AI agreement was good to excellent (ICC 0.76-0.92). Repeated measures analysis of expert and AI reports for the ascending aorta were moderate to good (ICC 0.57-0.88). AI diagnostic performance crossed the threshold for maximally accepted limits of agreement (&gt;5 mm) at the aortic root on ECG-gated CTs. AI newly identified aortic dilatation in 27% of patients on routine thoracic imaging with a specificity of 99% and sensitivity of 77%. CONCLUSION: AI has good agreement with expert readers at the mid-ascending aorta and has high specificity, but low sensitivity, at detecting dilated aortas on non-dedicated chest CTs. ADVANCES IN KNOWLEDGE: An AI tool may improve the detection of previously unknown thoracic aorta dilatation on chest CTs vs current routine reporting.</p

A Deep Learning-Based Radiomic Classifier for Usual Interstitial Pneumonia.

BACKGROUND: Because chest CT scan has largely supplanted surgical lung biopsy for diagnosing most cases of interstitial lung disease (ILD), tools to standardize CT scan interpretation are urgently needed. RESEARCH QUESTION: Does a deep learning (DL)-based classifier for usual interstitial pneumonia (UIP) derived using CT scan features accurately discriminate radiologist-determined visual UIP? STUDY DESIGN AND METHODS: A retrospective cohort study was performed. Chest CT scans acquired in individuals with and without ILD were drawn from a variety of public and private data sources. Using radiologist-determined visual UIP as ground truth, a convolutional neural network was used to learn discrete CT scan features of UIP, with outputs used to predict the likelihood of UIP using a linear support vector machine. Test performance characteristics were assessed in an independent performance cohort and multicenter ILD clinical cohort. Transplant-free survival was compared between UIP classification approaches using the Kaplan-Meier estimator and Cox proportional hazards regression. RESULTS: A total of 2,907 chest CT scans were included in the training (n&nbsp;= 1,934), validation (n&nbsp;= 408), and performance (n&nbsp;= 565) data sets. The prevalence of radiologist-determined visual UIP was 12.4%&nbsp;and 37.1%&nbsp;in the performance and ILD clinical cohorts, respectively. The DL-based UIP classifier predicted visual UIP in the performance cohort with sensitivity and specificity of 93%&nbsp;and 86%, respectively, and in the multicenter ILD clinical cohort with 81%&nbsp;and 77%, respectively. DL-based and visual UIP classification similarly discriminated survival, and outcomes were consistent among cases with positive DL-based UIP classification irrespective of visual classification. INTERPRETATION: A DL-based classifier for UIP demonstrated good test performance across a wide range of UIP prevalence and similarly discriminated survival when compared with radiologist-determined UIP. This automated tool could efficiently screen for UIP in patients undergoing chest CT scan and identify a high-risk phenotype among those with known ILD

Extra-cardiac findings in cardiovascular magnetic resonance:What the imaging cardiologist needs to know

Cardiovascular magnetic resonance (CMR) is an established non-invasive technique to comprehensively assess cardiovascular structure and function in a variety of acquired and inherited cardiac conditions. A significant amount of the neck, thorax and upper abdomen are imaged at the time of routine clinical CMR, particularly in the initial multi-slice axial and coronal images. The discovery of unsuspected disease at the time of imaging has ethical, financial and medico-legal implications. Extra-cardiac findings at the time of CMR are common, can be important and can change clinical management. Certain patient groups undergoing CMR are at particular risk of important extra-cardiac findings as several of the cardiovascular risk factors for atherosclerosis are also risk factors for malignancy. Furthermore, the presence of certain extra-cardiac findings may contribute to the interpretation of the primary cardiac pathology as some cardiac conditions have multi-systemic extra-cardiac involvement. The aim of this review is to give an overview of the type of extra-cardiac findings that may become apparent on CMR, subdivided by anatomical location. We focus on normal variant anatomy that may mimic disease, common incidental extra-cardiac findings and important imaging signs that help distinguish sinister pathology from benign disease. We also aim to provide a framework to the approach and potential further diagnostic work-up of incidental extra-cardiac findings discovered at the time of CMR. However, it is beyond the scope of this review to discuss and determine the clinical significance of extracardiac findings at CMR