Radiology
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Multicenter Study Observational Study
Coronary CT Angiography CAD-RADS versus Coronary Artery Calcium Score in Patients with Acute Chest Pain.
Background The Coronary Artery Disease Reporting and Data System (CAD-RADS) was established in 2016 to standardize the reporting of coronary artery disease at coronary CT angiography (CCTA). Purpose To assess the prognostic value of CAD-RADS at CCTA for major adverse cardiovascular events (MACEs) in patients presenting to the emergency department with chest pain. Materials and Methods This multicenter retrospective observational cohort study was conducted at four qualifying university teaching hospitals. ⋯ The C statistics revealed that the CAD-RADS score improved risk stratification more than that using clinical risk factors alone or combined with CACS (C-index, 0.85 vs 0.63 [P < .001] and 0.76 [P < .01], respectively). Conclusion The Coronary Artery Disease Reporting and Data System classification had an incremental prognostic value compared with the coronary artery calcium score in the prediction of major adverse cardiovascular events in patients presenting to the emergency department with acute chest pain. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Vliegenthart in this issue.
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Editorial Comment
Weight-bearing CT for Knee Osteoarthritis Assessment: A Story Unfolds.
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Randomized Controlled Trial
Added Value of Deep Learning-based Detection System for Multiple Major Findings on Chest Radiographs: A Randomized Crossover Study.
Background Previous studies assessing the effects of computer-aided detection on observer performance in the reading of chest radiographs used a sequential reading design that may have biased the results because of reading order or recall bias. Purpose To compare observer performance in detecting and localizing major abnormal findings including nodules, consolidation, interstitial opacity, pleural effusion, and pneumothorax on chest radiographs without versus with deep learning-based detection (DLD) system assistance in a randomized crossover design. Materials and Methods This study included retrospectively collected normal and abnormal chest radiographs between January 2016 and December 2017 (https://cris.nih.go.kr/; registration no. ⋯ Use of the DLD system improved the observers' JAFROC FOM (from 0.90 to 0.95, P = .002), AUC (from 0.93 to 0.98, P = .002), per-lesion sensitivity (from 83% [822 of 990 lesions] to 89.1% [882 of 990 lesions], P = .009), per-image sensitivity (from 80% [548 of 684 radiographs] to 89% [608 of 684 radiographs], P = .009), and specificity (from 89.3% [611 of 684 radiographs] to 96.6% [661 of 684 radiographs], P = .01) and reduced the reading time (from 10-65 seconds to 6-27 seconds, P < .001). The DLD system alone outperformed the pooled observers (JAFROC FOM: 0.96 vs 0.90, respectively, P = .007; AUC: 0.98 vs 0.93, P = .003). Conclusion Observers including thoracic radiologists showed improved performance in the detection and localization of major abnormal findings on chest radiographs and reduced reading time with use of a deep learning-based detection system. © RSNA, 2021 Online supplemental material is available for this article.
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Background The relationship between emphysema progression and long-term outcomes is unclear. Purpose To determine the relationship between emphysema progression at CT and mortality among participants with emphysema. Materials and Methods In a secondary analysis of two prospective observational studies, COPDGene (clinicaltrials.gov, NCT00608764) and Evaluation of Chronic Obstructive Pulmonary Disease Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE; clinicaltrials.gov, NCT00292552), emphysema was measured at CT at two points by using the volume-adjusted lung density at the 15th percentile of the lung density histogram (hereafter, lung density perc15) method. ⋯ In COPDGene, respiratory mortality increased by 22% (HR, 1.22; 95% CI: 1.13, 1.31; P < .001) for the same increase in the rate of change in lung density perc15. Conclusion In ever-smokers with emphysema, emphysema progression at CT was associated with increased all-cause and respiratory mortality. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Lee and Park in this issue.
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Background There are characteristic findings of coronavirus disease 2019 (COVID-19) on chest images. An artificial intelligence (AI) algorithm to detect COVID-19 on chest radiographs might be useful for triage or infection control within a hospital setting, but prior reports have been limited by small data sets, poor data quality, or both. Purpose To present DeepCOVID-XR, a deep learning AI algorithm to detect COVID-19 on chest radiographs, that was trained and tested on a large clinical data set. ⋯ With consensus interpretation as the reference standard, the AUC of DeepCOVID-XR was 0.95 (95% CI: 0.92, 0.98). Conclusion DeepCOVID-XR, an artificial intelligence algorithm, detected coronavirus disease 2019 on chest radiographs with a performance similar to that of experienced thoracic radiologists in consensus. © RSNA, 2020 Supplemental material is available for this article. See also the editorial by van Ginneken in this issue.