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Background: Accurate and rapid measurement of the MRI volume of meningiomas is essential in clinical practice to determine the growth rate of the tumor. Imperfect automation and disappointing performance for small meningiomas of previous automated volumetric tools limit their use in routine clinical practice. Purpose: To develop and validate a computational model for fully automated meningioma segmentation and volume measurement on contrast‐enhanced MRI scans using deep learning. Study Type: Retrospective. Population: A total of 659 intracranial meningioma patients (median age, 59.0 years; interquartile range: 53.0–66.0 years) including 554 women and 105 men. Field Strength/Sequence: The 1.0 T, 1.5 T, and 3.0 T; three‐dimensional, T1‐weighted gradient‐echo imaging with contrast enhancement. Assessment: The tumors were manually segmented by two neurosurgeons, H.K. and C.‐K.P., with 10 and 26 years of clinical experience, respectively, for use as the ground truth. Deep learning models based on U‐Net and nnU‐Net were trained using 459 subjects and tested for 100 patients from a single institution (internal validation set [IVS]) and 100 patients from other 24 institutions (external validation set [EVS]), respectively. The performance of each model was evaluated with the Sørensen‐Dice similarity coefficient (DSC) compared with the ground truth. Statistical Tests: According to the normality of the data distribution verified by the Shapiro–Wilk test, variables with three or more categories were compared by the Kruskal–Wallis test with Dunn's post hoc analysis. Results: A two‐dimensional (2D) nnU‐Net showed the highest median DSCs of 0.922 and 0.893 for the IVS and EVS, respectively. The nnU‐Nets achieved superior performance in meningioma segmentation than the U‐Nets. The DSCs of the 2D nnU‐Net for small meningiomas less than 1 cm3 were 0.769 and 0.780 with the IVS and EVS, respectively. Data Conclusion: A fully automated and accurate volumetric measurement tool for meningioma with clinically applicable performance for small meningioma using nnU‐Net was developed. Evidence Level: 3 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

• Generative adversarial network (GAN) for segmentation on chest radiographs were developed. • Developed GAN could extract pneumonia from actual chest radiographs of COVID-19 patients. • GAN-driven radiographic and CT-driven pneumonia extent showed strong correlation. • GAN-driven pneumonia extent was significant risk factor for unfavorable outcomes. To develop a generative adversarial network (GAN) to quantify COVID-19 pneumonia on chest radiographs automatically. This retrospective study included 50,000 consecutive non-COVID-19 chest CT scans in 2015–2017 for training. Anteroposterior virtual chest, lung, and pneumonia radiographs were generated from whole, segmented lung, and pneumonia pixels from each CT scan. Two GANs were sequentially trained to generate lung images from radiographs and to generate pneumonia images from lung images. GAN-driven pneumonia extent (pneumonia area/lung area) was expressed from 0% to 100%. We examined the correlation of GAN-driven pneumonia extent with semi-quantitative Brixia X-ray severity score (one dataset, n = 4707) and quantitative CT-driven pneumonia extent (four datasets, n = 54–375), along with analyzing a measurement difference between the GAN and CT extents. Three datasets (n = 243–1481), where unfavorable outcomes (respiratory failure, intensive care unit admission, and death) occurred in 10%, 38%, and 78%, respectively, were used to examine the predictive power of GAN-driven pneumonia extent. GAN-driven radiographic pneumonia was correlated with the severity score (0.611) and CT-driven extent (0.640). 95% limits of agreements between GAN and CT-driven extents were −27.1% to 17.4%. GAN-driven pneumonia extent provided odds ratios of 1.05–1.18 per percent for unfavorable outcomes in the three datasets, with areas under the receiver operating characteristic curve (AUCs) of 0.614–0.842. When combined with demographic information only and with both demographic and laboratory information, the prediction models yielded AUCs of 0.643–0.841 and 0.688–0.877, respectively. The generative adversarial network automatically quantified COVID-19 pneumonia on chest radiographs and identified patients with unfavorable outcomes. [ABSTRACT FROM AUTHOR]

Background Total lung capacity (TLC) has been estimated with use of chest radiographs based on time-consuming methods, such as planimetric techniques and manual measurements. Purpose To develop a deep learning-based, multidimensional model capable of estimating TLC from chest radiographs and demographic variables and validate its technical performance and clinical utility with use of multicenter retrospective data sets. Materials and Methods A deep learning model was pretrained with use of 50 000 consecutive chest CT scans performed between January 2015 and June 2017. The model was fine-tuned on 3523 pairs of posteroanterior chest radiographs and plethysmographic TLC measurements from consecutive patients who underwent pulmonary function testing on the same day. The model was tested with multicenter retrospective data sets from two tertiary care centers and one community hospital, including (a) an external test set 1 ( n = 207) and external test set 2 ( n = 216) for technical performance and (b) patients with idiopathic pulmonary fibrosis ( n = 217) for clinical utility. Technical performance was evaluated with use of various agreement measures, and clinical utility was assessed in terms of the prognostic value for overall survival with use of multivariable Cox regression. Results The mean absolute difference and within-subject SD between observed and estimated TLC were 0.69 L and 0.73 L, respectively, in the external test set 1 (161 men; median age, 70 years [IQR: 61-76 years]) and 0.52 L and 0.53 L in the external test set 2 (113 men; median age, 63 years [IQR: 51-70 years]). In patients with idiopathic pulmonary fibrosis (145 men; median age, 67 years [IQR: 61-73 years]), greater estimated TLC percentage was associated with lower mortality risk (adjusted hazard ratio, 0.97 per percent; 95% CI: 0.95, 0.98; P < .001). Conclusion A fully automatic, deep learning-based model estimated total lung capacity from chest radiographs, and the model predicted survival in idiopathic pulmonary fibrosis. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Sorkness in this issue.