Radiology最新文献

Background Climate change adversely affects human health, resulting in higher demand for health care services. However, the impact of climate-related environmental exposures on medical imaging utilization is currently unknown. Purpose To determine associations of short-term exposures to ambient heat and particulate air pollution with utilization of emergency department medical imaging. Materials and Methods In this retrospective time-stratified case-crossover study, daily imaging utilization counts from four emergency departments were linked to local daily environmental data-including fine particulate matter with 2.5-µm or smaller aerodynamic diameter (PM_2.5) and ambient temperature-over 10 years (January 2013 to December 2022). Conditional Poisson regression models were used to evaluate the associations between daily imaging utilization and environmental exposures on the same day and each of the 7 days preceding imaging, lag days 0-7, controlling for day of the week, month, and year. Moving averages of mean daily PM_2.5 and temperature were calculated to account for lagged exposure effects. Imaging counts were also stratified by modality (CT, radiography, US, and MRI). Results In an analysis of 1 666 420 emergency department imaging studies, a rise of 10 °C in the 2-day moving average of mean daily temperature and a rise of 10 μg/m³ in the 3-day moving average of mean daily PM_2.5 were associated with overall imaging utilization increases of 5.1% (incidence rate ratio [IRR], 1.051; 95% CI: 1.045, 1.056) and 4.0% (IRR, 1.040; 95% CI: 1.035, 1.046), respectively. Heat exposure days (mean temperature >20 °C) and air pollution exposure days (mean PM_2.5 >12 μg/m³) were associated with same-day excess absolute risk of 5.5 and 6.4 imaging studies per 1 million people at risk per day, respectively. Heat exposure days and air pollution exposure days were associated with increased utilization of radiography (excess relative risk, 2.7% [P < .001] and 2.1% [P < .001], respectively) and CT (excess relative risk, 2.0% [P = .001] and 2.7% [P < .001]) but not US (P = .14 and P = .14) or MRI (P = .70 and P = .65). Conclusion Short-term exposures to ambient heat and particulate air pollution were associated with increased utilization of radiography and CT but not US or MRI. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Vosshenrich in this issue.

Background Clinical outcome prediction based on acute-phase ischemic stroke data is valuable for planning health care resources, designing clinical trials, and setting patient expectations. Existing methods require individualized features and often involve manually engineered, time-consuming postprocessing activities. Purpose To predict the 90-day modified Rankin Scale (mRS) score with a deep learning (DL) model fusing noncontrast-enhanced CT (NCCT) and clinical information from the acute phase of stroke. Materials and Methods This retrospective study included data from six patient datasets from four multicenter trials and two registries. The DL-based imaging and clinical model was trained by using NCCT data obtained 1-7 days after baseline imaging and clinical data (age; sex; baseline and 24-hour National Institutes of Health Stroke Scale scores; and history of hypertension, diabetes, and atrial fibrillation). This model was compared with models based on either NCCT or clinical information alone. Model-specific mRS score prediction accuracy, mRS score accuracy within 1 point of the actual mRS score, mean absolute error (MAE), and performance in identifying unfavorable outcomes (mRS score, >2) were evaluated. Results A total of 1335 patients (median age, 71 years; IQR, 60-80 years; 674 female patients) were included for model development and testing through sixfold cross validation, with distributions of 979, 133, and 223 patients across training, validation, and test sets in each of the six cross-validation folds, respectively. The fused model achieved an MAE of 0.94 (95% CI: 0.89, 0.98) for predicting the specific mRS score, outperforming the imaging-only (MAE, 1.10; 95% CI: 1.05, 1.16; P < .001) and the clinical information-only (MAE, 1.00; 95% CI: 0.94, 1.05; P = .04) models. The fused model achieved an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI: 0.89, 0.92) for predicting unfavorable outcomes, outperforming the clinical information-only model (AUC, 0.88; 95% CI: 0.87, 0.90; P < .001) and the imaging-only model (AUC, 0.85; 95% CI: 0.84, 0.87; P < .001). Conclusion A fused DL-based NCCT and clinical model outperformed an imaging-only model and a clinical-information-only model in predicting 90-day mRS scores. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Lee in this issue.