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 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.
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Background In multiple sclerosis (MS), knowledge about how spinal cord abnormalities translate into clinical manifestations is incomplete. Comprehensive, multiparametric MRI studies are useful in this perspective, but studies for the spinal cord are lacking. Purpose To identify MRI features of cervical spinal cord damage that could help predict disability and disease course in MS by using a comprehensive, multiparametric MRI approach. ⋯ Cervical spinal cord GM atrophy is an accurate predictor of progressive phenotype. Cervical spinal cord GM lesions may subsequently cause GM atrophy, which may contribute to evolution to PMS. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Zivadinov and Bergsland in this issue.