T he reduction of lung cancer mortality by almost 20% in the National Lung Screening Trial can be partially attributed to the extensive use of low-dose CT for lung cancer screening in high-risk populations, which led to the improved detection of pulmonary nodules and early stage lung cancers (1,2). Pulmonary nodules are classified as solid, pure ground-glass, and part-solid nodules (PSNs) based on CT phenotyping, with PSNs being an important cancer predictor in the Brock model that is widely used to assess the malignant risk of pulmonary nodules (3). Moreover, adenocarcinomas manifesting as PSNs have been suggested to be a distinct subtype, most of which are confirmed as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IA) by abnormality, requiring a different management strategy due to different clinical-pathologic characteristics (4). Furthermore, evidence from histological specimens suggests that the solid components of lung nodules have a close-knit association with the invasive component of adenocarcinomas (5-7). Among the different subtypes of lung adenocarcinoma, IA has the worst prognosis, with the others having an almost 100% survival probability (8). Therefore, lobectomy is often recommended for patients with IA, whereas limited resections are suggested for patients with AIS or MIA (9).
Word count: 2973All rights reserved. No reuse allowed without permission.was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Key points:Question How do nomograms and machine-learning algorithms of severity risk prediction and triage of COVID- patients at hospital admission perform?Findings This model was prospectively validated on six test datasets comprising of 426 patients and yielded AUCs ranging from 0.816 to 0.976, accuracies ranging from 70.8% to 93.8%, sensitivities ranging from 83.7% to 100%, and specificities ranging from 41.0% to 95.7%. The cut-off probability values for low, medium, and high-risk groups were 0.072 and 0.244.Meaning The findings of this study suggest that our models performs well for the diagnosis and prediction of progression to severe or critical illness of COVID-19 patients and could be used for triage of COVID-19 patients at hospital admission.All rights reserved. No reuse allowed without permission.was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
Fossils from the Jehol Group (Early Cretaceous, Liaoning Province, China) are integral to our understanding of Paraves, the clade of dinosaurs grouping dromaeosaurids, troodontids, and avialians, including living birds. However, many taxa are represented by specimens of unclear ontogenetic age. Without a more thorough understanding of ontogeny, evolutionary relationships and significance of character states within paravian dinosaurs may be obscured and our ability to infer their biology restricted. We describe a complete specimen of a new microraptorine dromaeosaur, Wulong bohaiensis gen. et sp. nov., from the geologically young Jiufotang Formation (Aptian) that helps solve this problem. Phylogenetic analysis recovers the specimen within a monophyletic Microraptorinae. Preserved in articulation on a single slab, the type specimen is small and exhibits osteological markers of immaturity identified in other archosaurs, such as bone texture and lack of fusion. To contextualize this signal, we histologically sampled the tibia, fibula, and humerus and compared them with new samples from the closely related and osteologically mature Sinornithosaurus. Histology shows both specimens to be young and still growing at death, indicating an age for the new dinosaur of about 1 year. The holotype possesses several feather types, including filamentous feathers, pennaceous primaries, and long rectrices, establishing that their growth preceded skeletal maturity and full adult size in some dromaeosaurids. Comparison of histology in the new taxon and Sinornithosaurus indicates that macroscopic signs of maturity developed after the first year, but before cessation of growth, demonstrating that nonhistological indicators of adulthood may be misleading when applied to dromaeosaurids. Anat Rec, 303:963–987, 2020. © 2020 American Association for Anatomy
As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.
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