The value of the air bronchogram sign on CT image in the identification of different solitary pulmonary consolidation lesions

Qu, Huifang; Zhang, Wenchao; Yang, Jun; Jia, Shouqin; Wang, Guangbin

doi:10.1097/md.0000000000011985

Cited by 11 publications

(3 citation statements)

References 15 publications

(22 reference statements)

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“…The bright bronchogram seen in the area of diseased lung tissue is known as air bronchogram sign, which can be considered as strong evidence of in ammatory lesions. It has been reported that distal air bronchogram sign is helpful to distinguish the lung and pleura or mediastinal lesions,Alveolar lesions can be detected by air bronchogram sign, whereas thoracic reef and mediastinal lesions display no such signs [19][20]. So from this study, we observed the presence of more distal air bronchograms in the follow-up CT images of patients with exacerbation, suggesting that the lesion is further aggravated by expanding from the septal injury to the alveoli.…”

Section: Discussionsupporting

confidence: 50%

Predicting COVID-19 Disease Progression with Chest CT Images

Liang¹,

Qiu

Zhu

et al. 2020

Preprint

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Background: Some mild patients can deteriorate to moderate or severe within a week with the natural progression of COVID-19.it has been crucial to early identify those mild cases and give timely treatment . The chest computed tomography (CT) has shown to be useful to assist clinical diagnosis of COVID-19.In this study, machine learning was used to develop an early-warning CT feature model for predicting mild patients with potential malignant progression.Methods：The total of 140 COVID-19 mild patients were collected. All patients at admission were divided into groups (alleviation group and exacerbation group) with or without malignant progression.The clinical and laboratory data at admission, the first CT, and the follow-up CT at critical stage of the two groups were compared with Chi-square test,.The CT features data (distribution, morphology,etc) were used to establish the prediction model by Fisher's linear discriminant method and Unconditional logistic regression algorithm. And the model was validated with 40 exception data.and the Area Under ROC curve (AUC) was used to evaluate the models.Results：The model filtered out three variables of CT features including distal air bronchogram, fibrosis,and reversed halo sign. Notably, the distal air bronchograms was less common in alleviation group, while the fibrosis and reversed halo sign were more common.The sensitivity, specificity and Youden index of unconditional logistic regression were 86.1%, 92.6% and 78.7%, For the analysis of Fisher's linear discriminant, the sensitivity, specificity and Youden index were 83.3%, 94.1% and 77.4%. The generalization ability of both models were consistent with sensitivity of 95.89%, specificity of 100%, and Youden index of 83.33%.Conclusions: The CT imaging features-based machine learning model has a high sensitivity for finding out the mild patients who are easy to deteriorate into severe/critical cases efficiently so that timely treatments came true for those patients,while largely help to relieve the medical pressure.

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Section: Discussionsupporting

confidence: 50%

Predicting COVID-19 Disease Progression with Chest CT Images

Liang¹,

Qiu

Zhu

et al. 2020

Preprint

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“…A subsolid nodule with air bronchogram is supposedly more frequent with epidermal growth factor receptor (EGFR) mutation. As a typical sign of pneumonia, the air bronchography sign is important in the definitive diagnosis [31].…”

Section: Discussionmentioning

confidence: 99%

Chest

Jiang

Zhang

et al. 2020

Classic Imaging Signs

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Contents 4.1 Multinodule Accumulation Sign 4.2 Lobulation Sign 4.3 Spinous Protuberant Sign 4.4 Vacuole Sign 4.5 Coarse Spicules Sign 4.6 Pleural Indentation Sign 4.7 Beaded Septum Sign 4.8 Honeycomb Sign 4.9 Withered Tree Sign 4.10 CT Angiogram Sign 4.11 Air Bronchiologram Sign 4.12 Air Bronchogram 4.13 Positive Bronchus Sign 4.14 Mucous Bronchogram 4.15 Gloved Finger Sign 4.16 Feeding Vessel Sign 4.17 Vascular Convergence Sign 4.18 Silhouette Sign 4.19 Luftsichel Sign

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“…The product system is the core of the e-commerce platform, which can provide support and product information interaction for other systems. At the same time, cross-border e-commerce platforms are also the key to e-commerce business [13]. The rationality, stability, throughput, and scalability of the product system play an important role in determining whether the entire e-commerce platform will develop toward a high-quality platform.…”

Section: Aimage Recognition Of Cross-border E-commerce Productsmentioning

confidence: 99%