COVID-19 pneumonia chest radiographic severity score: variability assessment among experienced and in-training radiologists and creation of a multireader composite score database for artificial intelligence algorithm development.

OBJECTIVE: The purpose was to evaluate reader variability between experienced and in-training radiologists of COVID-19 pneumonia severity on chest radiograph (CXR), and to create a multireader database suitable for AI development. METHODS: In this study, CXRs from polymerase chain reaction positive COVID-19 patients were reviewed. Six experienced cardiothoracic radiologists and two residents classified each CXR according to severity. One radiologist performed the classification twice to assess intraobserver variability. Severity classification was assessed using a 4-class system: normal (0), mild (1), moderate (2), and severe (3). A median severity score (Rad Med) for each CXR was determined for the six radiologists for development of a multireader database (XCOMS). Kendal Tau correlation and percentage of disagreement were calculated to assess variability. RESULTS: A total of 397 patients (1208 CXRs) were included (mean age, 60 years SD ± 1), 189 men). Interobserver variability between the radiologists ranges between 0.67 and 0.78. Compared to the Rad Med score, the radiologists show good correlation between 0.79-0.88. Residents show slightly lower interobserver agreement of 0.66 with each other and between 0.69 and 0.71 with experienced radiologists. Intraobserver agreement was high with a correlation coefficient of 0.77. In 220 (18%), 707 (59%), 259 (21%) and 22 (2%) CXRs there was a 0, 1, 2 or 3 class-difference. In 594 (50%) CXRs the median scores of the residents and the radiologists were similar, in 578 (48%) and 36 (3%) CXRs there was a 1 and 2 class-difference. CONCLUSION: Experienced and in-training radiologists demonstrate good inter- and intraobserver agreement in COVID-19 pneumonia severity classification. A higher percentage of disagreement was observed in moderate cases, which may affect training of AI algorithms. ADVANCES IN KNOWLEDGE: Most AI algorithms are trained on data labeled by a single expert. This study shows that for COVID-19 X-ray severity classification there is significant variability and disagreement between radiologist and between residents.

MRI perfusion in patients with stable chest-pain.

Perfusion-cardiovascular MR (CMR) imaging has been shown to reliably identify patients with suspected or known coronary artery disease (CAD), who are at risk for future cardiac events and thus, allows for guiding therapy including revascularizations. Accordingly, it is an ideal test to exclude prognostically relevant coronary artery disease. Several guidelines, such as the ESC guidelines, currently recommend CMR as non-invasive testing in patients with stable chest pain. CMR has as an advantage over the more conventional pathways as it lacks radiation and it potentially reduces costs.

Current and future applications of CT coronary calcium assessment.

INTRODUCTION: Computed tomographic (CT) coronary artery calcium scoring (CAC) has been validated as a well-established screening method for cardiovascular risk stratification and treatment management that is used in addition to traditional risk factors. The purpose of this review is to present an update on current and future applications of CAC. Areas covered: The topic of CAC is summarized from its introduction to current application with focus on the validation and clinical integration including cardiovascular risk prediction and outcome, cost-effectiveness, impact on downstream medical testing, and the technical advances in scanner and software technology that are shaping the future of CAC. Furthermore, this review aims to provide guidance for the appropriate clinical use of CAC. Expert commentary: CAC is a well-established screening test in preventive care that is underused in daily clinical practice. The widespread clinical implementation of CAC will be decided by future technical advances in CT image acquisition, cost-effectiveness, and reimbursement status.