Radiology
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BackgroundPattern of emphysema at chest CT, scored visually by using the Fleischner Society system, is associated with physiologic impairment and mortality risk. PurposeTo determine whether participant-level emphysema pattern could predict impairment and mortality when classified by using a deep learning method. Materials and MethodsThis retrospective analysis of Genetic Epidemiology of COPD (COPDGene) study participants enrolled between 2007 and 2011 included those with baseline CT, visual emphysema scores, and survival data through 2018. ⋯ In the COPDGene test cohort, deep learning emphysema classification improved the fit of linear mixed models in the prediction of these clinical parameters compared with visual scoring (P < .001). Compared with participants without emphysema, mortality was greater in participants classified by the deep learning algorithm as having any grade of emphysema (adjusted hazard ratios were 1.5, 1.7, 2.9, 5.3, and 9.7, respectively, for trace, mild, moderate, confluent, and advanced destructive emphysema; P < .05). ConclusionDeep learning automation of the Fleischner grade of emphysema at chest CT is associated with clinical measures of pulmonary insufficiency and the risk of mortality.© RSNA, 2019Online supplemental material is available for this article.
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Background Signal contamination from long T2 water is a major challenge in direct imaging of myelin with MRI. Nulling of the unwanted long T2 signals can be achieved with an inversion recovery (IR) preparation pulse to null long T2 white matter within the brain. The remaining ultrashort T2 signal from myelin can be detected with an ultrashort echo time (UTE) sequence. ⋯ In the human study, there was a significant reduction in normalized signal intensity in MS lesions compared with that in normal-appearing white matter (0.19 ± 0.10 vs 0.76 ± 0.11, respectively; P < .001). Conclusion The double-echo sliding inversion recovery ultrashort echo time sequence can generate whole-brain myelin images specifically with a clinical 3-T scanner. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Port in this issue.
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Background Screening that includes digital breast tomosynthesis (DBT) with two-dimensional (2D) synthetic mammography (SM) or standard 2D digital mammography (DM) results in detection of more breast cancers than does screening with DM alone. A decrease in interval breast cancer rates is anticipated but is not reported. Purpose To compare rates and characteristics of (a) interval breast cancer in women screened with DBT and SM versus those screened with DM alone and (b) screen-detected breast cancer at consecutive screenings with DM. ⋯ Rates of histologic grade 1 invasive cancer were 0.5 per 1000 screened women (study group) and 1.3 per 1000 screened women (control group) (P = .001). Conclusion No differences in interval breast cancer rates or tumor characteristics were observed in women screened with DBT and SM compared with women screened with DM. Higher rates of low-grade screen-detected tumors were observed in the control group at consecutive screening. © RSNA, 2019 Online supplemental material is available for this article.
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Background Enhancing lesions on MRI scans obtained after contrast material administration are commonly thought to represent disease activity in multiple sclerosis (MS); it is desirable to develop methods that can predict enhancing lesions without the use of contrast material. Purpose To evaluate whether deep learning can predict enhancing lesions on MRI scans obtained without the use of contrast material. Materials and Methods This study involved prospective analysis of existing MRI data. ⋯ The corresponding participant-wise values were 72% ± 9.0 and 70% ± 6.3. The diagnostic performances (AUCs) were 0.82 ± 0.02 and 0.75 ± 0.03 for slice-wise and participant-wise enhancement prediction, respectively. Conclusion Deep learning used with conventional MRI identified enhanced lesions in multiple sclerosis from images from unenhanced multiparametric MRI with moderate to high accuracy. © RSNA, 2019.
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Background Fluorine 18 (18F)-fluorodeoxyglucose (FDG) PET/CT is a routine tool for staging patients with lymphoma and lung cancer. Purpose To evaluate configurations of deep convolutional neural networks (CNNs) to localize and classify uptake patterns of whole-body 18F-FDG PET/CT images in patients with lung cancer and lymphoma. Materials and Methods This was a retrospective analysis of consecutive patients with lung cancer or lymphoma referred to a single center from August 2011 to August 2013. ⋯ Anatomic localization accuracy of the CNN was 2543 of 2639 (96.4%; 95% CI: 95.5%, 97.1%) for body part, 2292 of 2639 (86.9%; 95% CI: 85.3%, 88.5%) for region (ie, organ), and 2149 of 2639 (81.4%; 95% CI: 79.3%-83.5%) for subregion. Conclusion The fully automated anatomic localization and classification of fluorine 18-fluorodeoxyglucose PET uptake patterns in foci suspicious and nonsuspicious for cancer in patients with lung cancer and lymphoma by using a convolutional neural network is feasible and achieves high diagnostic performance when both CT and PET images are used. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Froelich and Salavati in this issue.