Radiology
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Background Recent studies have suggested that chest CT scans could be used as a primary screening or diagnostic tool for coronavirus disease 2019 (COVID-19) in epidemic areas. Purpose To perform a meta-analysis to evaluate diagnostic performance measures, including predictive values of chest CT and initial reverse transcriptase polymerase chain reaction (RT-PCR). Materials and Methods Medline and Embase were searched from January 1, 2020, to April 3, 2020, for studies on COVID-19 that reported the sensitivity, specificity, or both of CT scans, RT-PCR assays, or both. ⋯ The sensitivity of CT was affected by the distribution of disease severity, the proportion of patients with comorbidities, and the proportion of asymptomatic patients (all P < .05). The sensitivity of RT-PCR was negatively associated with the proportion of elderly patients (P = .01). Conclusion Outside of China where there is a low prevalence of coronavirus disease 2019 (range, 1%-22.9%), chest CT screening of patients with suspected disease had low positive predictive value (range, 1.5%-30.7%). © RSNA, 2020 Online supplemental material is available for this article.
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Background Atypical manifestations of coronavirus disease 2019 (COVID-19) are being encountered as the pandemic unfolds, leading to non-chest CT scans that may uncover unsuspected pulmonary disease. Purpose To investigate patients with primary nonrespiratory symptoms who underwent CT of the abdomen or pelvis or CT of the cervical spine or neck with unsuspected findings highly suspicious for pulmonary COVID-19. Materials and Methods This retrospective study from March 10, 2020, to April 6, 2020, involved three institutions, two in a region considered a hot spot (area of high prevalence) for COVID-19. ⋯ Major interventions (vasopressor medication or intubation) were required for 29 of 119 (24%) patients, and 27 of 119 (23%) died. Patients who underwent CT of the cervical spine or neck had worse outcomes than those who underwent abdominal or pelvic CT (P = .01). Conclusion In a substantial percentage of patients with primary nonrespiratory symptoms who underwent non-chest CT, CT provided evidence of coronavirus disease 2019-related pneumonia. © RSNA, 2020.
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Background It is uncertain whether a deep learning-based automatic detection algorithm (DLAD) for identifying malignant nodules on chest radiographs will help diagnose lung cancers. Purpose To evaluate the efficacy of using a DLAD in observer performance for the detection of lung cancers on chest radiographs. Materials and Methods Among patients diagnosed with lung cancers between January 2010 and December 2014, 117 patients (median age, 69 years; interquartile range [IQR], 64-74 years; 57 women) were retrospectively identified in whom lung cancers were visible on previous chest radiographs. ⋯ With a DLAD, observers detected more overlooked lung cancers (average sensitivity, 53% [56 of 105 patients] with a DLAD vs 40% [42 of 105 patients] without a DLAD) (P < .001) and recommended chest CT for more patients (62% [66 of 105 patients] with a DLAD vs 47% [49 of 105 patients] without a DLAD) (P < .001). In the healthy control group, no difference existed in the rate of chest CT recommendation (10% [23 of 234 patients] without a DLAD and 8% [20 of 234 patients] with a DLAD) (P = .13). Conclusion Using a deep learning-based automatic detection algorithm may help observers reduce the number of overlooked lung cancers on chest radiographs, without a proportional increase in the number of follow-up chest CT examinations. © RSNA, 2020 Online supplemental material is available for this article.