BackgroundGround‐glass nodules (GGNs), which are possible precursors of lung cancer, attract increasing attention. Many studies have attempted to identify the characteristic imaging features of GGNs for their qualitative diagnosis; however, the comprehension of GGNs remains controversial. We performed this study to identify imaging characteristics helpful to the differential diagnosis of solitary GGNs.MethodsWe retrospectively evaluated 112 solitary GGNs resected from 112 patients, pathologically examined after surgical resection. Imaging features of the GGNs, such as size, shape, a solid component, lobulation, spiculation, vascular convergence sign, pleural tag, and air cavity density, were assessed. Differences between malignant and benign nodules were analyzed using binary logistic regression analysis.ResultsOf the 112 GGNs, 82 were malignant and 30 were benign. A solid component, vascular convergence sign, and a larger diameter were risk factors for malignancy, with a sensitivity, specificity, and accuracy of 93.9%, 60.0%, and 84.8%, respectively. Lobulation, spiculation, air cavity densities, and pleural tags were also important indicators of malignancy, with positive predictive values of 93.5%, 83.3%, 91.7%, and 87.2%, respectively.Conclusion GGNs with a solid component, vascular convergence sign, and a larger diameter are highly suggestive of malignancy. The possibility of a neoplasm should also be considered in the case of GGNs that show lobulation, spiculation, air cavity densities, or pleural tags. To obtain a comprehensive and accurate analysis of the nodules, three‐dimensional reconstruction is highly recommended.

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International audienceWandering is among the most frequent, problematic, and dangerous behaviors for elders with dementia. Frequent wanderers likely suffer falls and fractures, which affect the safety and quality of their lives. In order to monitor outdoor wandering of elderly people with dementia, this paper proposes a real-time method for wandering detection based on individuals' GPS traces. By representing wandering traces as loops, the problem of wandering detection is transformed into detecting loops in elders' mobility trajectories. Specifically, the raw GPS data is first preprocessed to remove noisy and crowded points by performing an online mean shift clustering. A novel method called θ_WD is then presented that is able to detect loop-like traces on the fly. The experimental results on the GPS datasets of several elders have show that the θ_WD method is effective and efficient in detecting wandering behaviors, in terms of detection performance (AUC > 0.99, and 90% detection rate with less than 5 % of the false alarm rate), as well as time complexit

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